Filtering ASVs based on dissimilarity across sample replicates
20 May 2021
Here we’ll use the Bray-Curtis index to identify sample replicates (within each locus) that are more dissimilar than similar to the other replicates. Dissimilarity can be an indication of an issue (contamination, etc.) with a particular replicate.
This uses the output from the species-occupancy detection modeling in 03-species-occupancy-model.Rmd The occupancy modeling uses the ASVs (not taxonomy) and I’ll use a similar approach here with the Bray-Curtis and NMDS analyses.
Process
The process is a bit cumbersome because it requires looking at each sample/locus/replicate for the full reference DNA pool and vouchered reference pool.
The process for looking at the dissimilarity among replicates is to: 1. Read in data that has been cleaned up using the occupancy modeling 2. Create a community matrix (per locus) 3. Standardize data across replicates (fct decostand) 4. Generate Bray-Curtis distances (fct vegdist) 4a. Are any replicates more dissimilar than similar? 5. Generate NMDS plots from distance matrix (fct metaMDS)
- Read in list
Outputs
Based on the NMDS plots and Bray-Curtis dissimilarity index, I generated a list of samples to remove:
../data/reference_pool_dissimilarity_samples_to_remove.csv
The data that were used to generate that list are output .csv files from the Bray-Curtis function, implemented below.
In addition, three loci had insufficient data across all 18 samples in both the vouchered and full reference pools to be included in these analyses and will be dropped from further analyses:
16Sfish teleo crust2
source("../R/metabarcoding-funcs.R")library(tidyverse)
library(stringi)
library(vegan)
library(reshape2)
library(textshape)
library(rlist)
# output from the ASV filtering based on the SODM for
# vouchered ref
vrp_sodm_filtered_df <- readRDS("../extdata/downsampled_loci/data/voucher_features_sodm_filtered_taxonomy_df.rds")
# full reference
frp_sodm_filtered_df <- readRDS("../extdata/downsampled_loci/data/full_reference_sodm_filtered_taxonomy_df.rds")
I have wrapped the Bray-Curis and NMDS up into a function called bray_nmds_complete which outputs a .csv file with the replicates that are > 0.49 dissimilar and generates an NMDS plot.
I’ll use that function with an lappy and the list of loci, since each locus will be analyzed separately.
To cycle over a list of the loci…
loc_list19
[1] "16SH1" "16Svar" "18SSSU3" "18Sn4" "L2513H2714" "aquaF2"
[7] "aquaF3" "cep" "ceph16S" "crust16S" "fishcoilbc" "fishminiA"
[13] "mifish" "minibar" "nsCOIFo" "plankCOI" "shark474" "sharkCOImini"
[19] "short28S" Full Reference dataframe
# cycle over the list of loci for the full reference pool sample replicates
# using the bray-curtis function to test for dissimilarity
lapply(loc_list19, bray_nmds_complete, sodm_filtered_df = frp_sodm_filtered_df, sample = "FRP")`summarise()` has grouped output by 'seq'. You can override using the `.groups` argument.
some squared distances are negative and changed to zeroRun 0 stress 0.02549398
Run 1 stress 0.06753572
Run 2 stress 0.04566323
Run 3 stress 0.02549088
... New best solution
... Procrustes: rmse 0.001081767 max resid 0.001961006
... Similar to previous best
Run 4 stress 0.04566343
Run 5 stress 0.04566312
Run 6 stress 0.1679505
Run 7 stress 0.2212383
Run 8 stress 0.1679505
Run 9 stress 0.06753611
Run 10 stress 0.06611416
Run 11 stress 0.06753527
Run 12 stress 0.0254977
... Procrustes: rmse 0.003715123 max resid 0.007017285
... Similar to previous best
Run 13 stress 0.06753667
Run 14 stress 0.06611411
Run 15 stress 0.02549271
... Procrustes: rmse 0.001928513 max resid 0.003693382
... Similar to previous best
Run 16 stress 0.1675538
Run 17 stress 0.06611438
Run 18 stress 0.04566336
Run 19 stress 0.02549439
... Procrustes: rmse 0.00138344 max resid 0.002461619
... Similar to previous best
Run 20 stress 0.3017353
Run 21 stress 0.02549458
... Procrustes: rmse 0.001435473 max resid 0.002553668
... Similar to previous best
Run 22 stress 0.06753608
Run 23 stress 0.06611407
Run 24 stress 0.02549067
... New best solution
... Procrustes: rmse 0.0002607035 max resid 0.0004446153
... Similar to previous best
*** Solution reached
`summarise()` has grouped output by 'seq'. You can override using the `.groups` argument.
Run 0 stress 0.06789099
Run 1 stress 0.06116551
... New best solution
... Procrustes: rmse 0.1716614 max resid 0.3019298
Run 2 stress 0.06116556
... Procrustes: rmse 7.298398e-05 max resid 0.0001426581
... Similar to previous best
Run 3 stress 0.3209238
Run 4 stress 0.06116557
... Procrustes: rmse 3.670787e-05 max resid 6.069345e-05
... Similar to previous best
Run 5 stress 0.06116551
... Procrustes: rmse 1.749457e-05 max resid 3.085404e-05
... Similar to previous best
Run 6 stress 0.08239048
Run 7 stress 0.06116551
... Procrustes: rmse 1.75612e-05 max resid 2.877174e-05
... Similar to previous best
Run 8 stress 0.2043563
Run 9 stress 0.06789111
Run 10 stress 0.06116551
... New best solution
... Procrustes: rmse 9.187778e-06 max resid 1.529488e-05
... Similar to previous best
Run 11 stress 0.08239083
Run 12 stress 0.06789132
Run 13 stress 0.08239047
Run 14 stress 0.06116551
... New best solution
... Procrustes: rmse 7.046014e-06 max resid 1.072198e-05
... Similar to previous best
Run 15 stress 0.06116552
... Procrustes: rmse 3.771754e-05 max resid 7.569807e-05
... Similar to previous best
Run 16 stress 0.1681478
Run 17 stress 0.0678909
Run 18 stress 0.06789153
Run 19 stress 0.06116551
... New best solution
... Procrustes: rmse 1.084345e-05 max resid 1.715373e-05
... Similar to previous best
Run 20 stress 0.06116551
... Procrustes: rmse 6.06084e-06 max resid 9.507563e-06
... Similar to previous best
Run 21 stress 0.06789127
Run 22 stress 0.06789101
Run 23 stress 0.06789113
Run 24 stress 0.06789112
*** Solution reached
`summarise()` has grouped output by 'seq'. You can override using the `.groups` argument.
Run 0 stress 9.579796e-05
Run 1 stress 9.427029e-05
... New best solution
... Procrustes: rmse 0.02247895 max resid 0.04396397
Run 2 stress 8.642301e-05
... New best solution
... Procrustes: rmse 0.1175535 max resid 0.2402706
Run 3 stress 8.540527e-05
... New best solution
... Procrustes: rmse 0.09818878 max resid 0.1935
Run 4 stress 9.678334e-05
... Procrustes: rmse 0.08816036 max resid 0.1795636
Run 5 stress 9.650868e-05
... Procrustes: rmse 0.03322299 max resid 0.06630169
Run 6 stress 9.280767e-05
... Procrustes: rmse 0.001147417 max resid 0.002240407
... Similar to previous best
Run 7 stress 9.748182e-05
... Procrustes: rmse 0.0966331 max resid 0.1974143
Run 8 stress 8.37519e-05
... New best solution
... Procrustes: rmse 0.1103649 max resid 0.2095578
Run 9 stress 7.870168e-05
... New best solution
... Procrustes: rmse 0.03600446 max resid 0.06782502
Run 10 stress 8.075581e-05
... Procrustes: rmse 0.03398 max resid 0.0644858
Run 11 stress 9.517662e-05
... Procrustes: rmse 0.2313203 max resid 0.4739927
Run 12 stress 6.63805e-05
... New best solution
... Procrustes: rmse 0.03534882 max resid 0.06626279
Run 13 stress 9.683091e-05
... Procrustes: rmse 0.1111087 max resid 0.2135577
Run 14 stress 8.881884e-05
... Procrustes: rmse 0.2341704 max resid 0.474671
Run 15 stress 8.930062e-05
... Procrustes: rmse 0.244706 max resid 0.4986702
Run 16 stress 9.769992e-05
... Procrustes: rmse 0.04268541 max resid 0.08052105
Run 17 stress 8.760182e-05
... Procrustes: rmse 0.069625 max resid 0.1321595
Run 18 stress 9.781914e-05
... Procrustes: rmse 0.07685473 max resid 0.1461738
Run 19 stress 8.628186e-05
... Procrustes: rmse 0.2465064 max resid 0.502787
Run 20 stress 9.733444e-05
... Procrustes: rmse 0.2519811 max resid 0.5153881
Run 21 stress 9.374369e-05
... Procrustes: rmse 0.2539169 max resid 0.5198679
Run 22 stress 9.943772e-05
... Procrustes: rmse 0.02757721 max resid 0.05187796
Run 23 stress 9.467886e-05
... Procrustes: rmse 0.2388507 max resid 0.485292
Run 24 stress 9.9242e-05
... Procrustes: rmse 0.0791815 max resid 0.1506837
Run 25 stress 8.403568e-05
... Procrustes: rmse 0.03850305 max resid 0.07203395
Run 26 stress 9.98365e-05
... Procrustes: rmse 0.1769452 max resid 0.349054
Run 27 stress 9.877062e-05
... Procrustes: rmse 0.01073389 max resid 0.0201402
Run 28 stress 9.577971e-05
... Procrustes: rmse 0.00574002 max resid 0.01074771
Run 29 stress 9.874117e-05
... Procrustes: rmse 0.237319 max resid 0.4818348
Run 30 stress 9.869722e-05
... Procrustes: rmse 0.2541792 max resid 0.5204795
Run 31 stress 9.232483e-05
... Procrustes: rmse 0.247805 max resid 0.5057687
Run 32 stress 9.932195e-05
... Procrustes: rmse 0.2481882 max resid 0.5066865
Run 33 stress 9.034751e-05
... Procrustes: rmse 0.157195 max resid 0.307463
Run 34 stress 7.855983e-05
... Procrustes: rmse 0.212911 max resid 0.4270818
Run 35 stress 9.517238e-05
... Procrustes: rmse 0.09108466 max resid 0.1739436
Run 36 stress 9.564499e-05
... Procrustes: rmse 0.2382372 max resid 0.4839096
Run 37 stress 9.858864e-05
... Procrustes: rmse 0.248585 max resid 0.5075672
Run 38 stress 9.447921e-05
... Procrustes: rmse 0.1255467 max resid 0.2425351
Run 39 stress 9.114647e-05
... Procrustes: rmse 0.2509805 max resid 0.5130703
Run 40 stress 8.99543e-05
... Procrustes: rmse 0.02864414 max resid 0.05389806
Run 41 stress 9.885274e-05
... Procrustes: rmse 0.1563639 max resid 0.3057428
Run 42 stress 9.716382e-05
... Procrustes: rmse 0.2539709 max resid 0.5199975
Run 43 stress 9.892495e-05
... Procrustes: rmse 0.07448279 max resid 0.1415641
Run 44 stress 9.385376e-05
... Procrustes: rmse 0.1123675 max resid 0.2160528
Run 45 stress 9.566503e-05
... Procrustes: rmse 0.2492487 max resid 0.5091057
Run 46 stress 9.824921e-05
... Procrustes: rmse 0.09186905 max resid 0.1754727
Run 47 stress 6.942604e-05
... Procrustes: rmse 0.07190642 max resid 0.1365706
Run 48 stress 8.646939e-05
... Procrustes: rmse 0.244192 max resid 0.4974864
Run 49 stress 8.255958e-05
... Procrustes: rmse 0.1158942 max resid 0.2231095
Run 50 stress 9.87686e-05
... Procrustes: rmse 0.080067 max resid 0.1524091
Run 51 stress 9.647351e-05
... Procrustes: rmse 0.2502516 max resid 0.5114115
Run 52 stress 9.976061e-05
... Procrustes: rmse 0.243254 max resid 0.4953604
Run 53 stress 8.374791e-05
... Procrustes: rmse 0.01420416 max resid 0.02666688
Run 54 stress 9.085281e-05
... Procrustes: rmse 0.1429225 max resid 0.2779268
Run 55 stress 9.928144e-05
... Procrustes: rmse 0.0346385 max resid 0.06480249
Run 56 stress 9.685915e-05
... Procrustes: rmse 0.09805547 max resid 0.1876499
*** No convergence -- monoMDS stopping criteria:
56: stress < sminstress is (nearly) zero: you may have insufficient data
`summarise()` has grouped output by 'seq'. You can override using the `.groups` argument.
some squared distances are negative and changed to zero
Run 0 stress 0.03982062
Run 1 stress 0.03982118
... Procrustes: rmse 0.0005872108 max resid 0.0008446614
... Similar to previous best
Run 2 stress 0.08670257
Run 3 stress 0.09990103
Run 4 stress 0.07941983
Run 5 stress 0.05583793
Run 6 stress 0.04111592
Run 7 stress 0.04111487
Run 8 stress 0.05583784
Run 9 stress 0.03982081
... Procrustes: rmse 0.0003801426 max resid 0.0005443656
... Similar to previous best
Run 10 stress 0.03982142
... Procrustes: rmse 0.0006806971 max resid 0.0009800321
... Similar to previous best
Run 11 stress 0.04111577
Run 12 stress 0.03982075
... Procrustes: rmse 0.0003134993 max resid 0.0004214406
... Similar to previous best
Run 13 stress 0.03982068
... Procrustes: rmse 0.0002667816 max resid 0.0003818783
... Similar to previous best
Run 14 stress 0.05700056
Run 15 stress 0.03982074
... Procrustes: rmse 0.0002821026 max resid 0.0004022993
... Similar to previous best
Run 16 stress 0.04111488
Run 17 stress 0.09992496
Run 18 stress 0.05583784
Run 19 stress 0.04111507
Run 20 stress 0.03982186
... Procrustes: rmse 0.0008269277 max resid 0.001192183
... Similar to previous best
Run 21 stress 0.05700057
Run 22 stress 0.03982156
... Procrustes: rmse 0.0007155026 max resid 0.00102945
... Similar to previous best
Run 23 stress 0.09992516
Run 24 stress 0.09990102
*** Solution reached
`summarise()` has grouped output by 'seq'. You can override using the `.groups` argument.
some squared distances are negative and changed to zero
Run 0 stress 0.08062364
Run 1 stress 0.08062463
... Procrustes: rmse 0.001414915 max resid 0.00365458
... Similar to previous best
Run 2 stress 0.08062483
... Procrustes: rmse 0.00168243 max resid 0.004292421
... Similar to previous best
Run 3 stress 0.08062476
... Procrustes: rmse 0.001613128 max resid 0.004175612
... Similar to previous best
Run 4 stress 0.08062494
... Procrustes: rmse 0.001838937 max resid 0.004666044
... Similar to previous best
Run 5 stress 0.08315292
Run 6 stress 0.08315048
Run 7 stress 0.08315198
Run 8 stress 0.08315018
Run 9 stress 0.08062457
... Procrustes: rmse 0.0007818514 max resid 0.001987424
... Similar to previous best
Run 10 stress 0.1402712
Run 11 stress 0.08315043
Run 12 stress 0.08062181
... New best solution
... Procrustes: rmse 0.002392605 max resid 0.006193983
... Similar to previous best
Run 13 stress 0.08315058
Run 14 stress 0.08062424
... Procrustes: rmse 0.002795577 max resid 0.007395728
... Similar to previous best
Run 15 stress 0.08315021
Run 16 stress 0.08315125
Run 17 stress 0.08315049
Run 18 stress 0.1284401
Run 19 stress 0.08315046
Run 20 stress 0.08062452
... Procrustes: rmse 0.003649667 max resid 0.009453231
... Similar to previous best
Run 21 stress 0.08315019
Run 22 stress 0.1402707
Run 23 stress 0.08062456
... Procrustes: rmse 0.003665198 max resid 0.009547438
... Similar to previous best
Run 24 stress 0.1284401
*** Solution reached
`summarise()` has grouped output by 'seq'. You can override using the `.groups` argument.
Run 0 stress 0.06013984
Run 1 stress 0.185523
Run 2 stress 0.06013986
... Procrustes: rmse 5.603937e-05 max resid 0.0001059202
... Similar to previous best
Run 3 stress 0.1201634
Run 4 stress 0.06013984
... Procrustes: rmse 6.64174e-06 max resid 1.120539e-05
... Similar to previous best
Run 5 stress 0.06013984
... Procrustes: rmse 3.797987e-06 max resid 5.498389e-06
... Similar to previous best
Run 6 stress 0.06013984
... Procrustes: rmse 8.835351e-06 max resid 1.397334e-05
... Similar to previous best
Run 7 stress 0.06013984
... New best solution
... Procrustes: rmse 1.561536e-06 max resid 1.971981e-06
... Similar to previous best
Run 8 stress 0.3209238
Run 9 stress 0.06013984
... Procrustes: rmse 1.126421e-05 max resid 2.023166e-05
... Similar to previous best
Run 10 stress 0.1191491
Run 11 stress 0.06013984
... Procrustes: rmse 2.768543e-06 max resid 5.679992e-06
... Similar to previous best
Run 12 stress 0.07031996
Run 13 stress 0.06013985
... Procrustes: rmse 2.459416e-05 max resid 4.161665e-05
... Similar to previous best
Run 14 stress 0.06013984
... Procrustes: rmse 1.482755e-06 max resid 2.629661e-06
... Similar to previous best
Run 15 stress 0.07031998
Run 16 stress 0.06013984
... Procrustes: rmse 1.189837e-05 max resid 2.170195e-05
... Similar to previous best
Run 17 stress 0.1191486
Run 18 stress 0.1191486
Run 19 stress 0.1201632
Run 20 stress 0.06013984
... Procrustes: rmse 4.230758e-06 max resid 9.107742e-06
... Similar to previous best
Run 21 stress 0.1201645
Run 22 stress 0.120164
Run 23 stress 0.06013984
... Procrustes: rmse 1.711242e-06 max resid 3.205746e-06
... Similar to previous best
Run 24 stress 0.06013984
... Procrustes: rmse 2.18281e-06 max resid 4.086623e-06
... Similar to previous best
*** Solution reached
`summarise()` has grouped output by 'seq'. You can override using the `.groups` argument.
Run 0 stress 0.03577716
Run 1 stress 0.04651979
Run 2 stress 0.03577555
... New best solution
... Procrustes: rmse 0.001549247 max resid 0.003208737
... Similar to previous best
Run 3 stress 0.03577502
... New best solution
... Procrustes: rmse 0.0007433974 max resid 0.001590529
... Similar to previous best
Run 4 stress 0.05969502
Run 5 stress 0.05753571
Run 6 stress 0.0357749
... New best solution
... Procrustes: rmse 0.0004667569 max resid 0.0009666398
... Similar to previous best
Run 7 stress 0.04651983
Run 8 stress 0.03577568
... Procrustes: rmse 0.0007953663 max resid 0.001686929
... Similar to previous best
Run 9 stress 0.05753603
Run 10 stress 0.05753569
Run 11 stress 0.0357751
... Procrustes: rmse 0.0002701018 max resid 0.0005744852
... Similar to previous best
Run 12 stress 0.05753568
Run 13 stress 0.04264545
Run 14 stress 0.04255826
Run 15 stress 0.3011211
Run 16 stress 0.03577486
... New best solution
... Procrustes: rmse 0.0002169768 max resid 0.0004502314
... Similar to previous best
Run 17 stress 0.04255852
Run 18 stress 0.05753569
Run 19 stress 0.04651982
Run 20 stress 0.04255823
Run 21 stress 0.0575357
Run 22 stress 0.05753577
Run 23 stress 0.05969527
Run 24 stress 0.05969511
*** Solution reached
`summarise()` has grouped output by 'seq'. You can override using the `.groups` argument.
Run 0 stress 0.002232437
Run 1 stress 0.001997919
... New best solution
... Procrustes: rmse 0.03398822 max resid 0.0802936
Run 2 stress 0.002874878
Run 3 stress 9.352181e-05
... New best solution
... Procrustes: rmse 0.03610696 max resid 0.05347434
Run 4 stress 9.795779e-05
... Procrustes: rmse 0.000109017 max resid 0.0002649567
... Similar to previous best
Run 5 stress 0.2795898
Run 6 stress 0.002084132
Run 7 stress 0.003914176
Run 8 stress 0.0009390012
Run 9 stress 0.0003463769
... Procrustes: rmse 0.00668874 max resid 0.01001875
Run 10 stress 9.402995e-05
... Procrustes: rmse 0.0001740919 max resid 0.0003706251
... Similar to previous best
Run 11 stress 0.001612211
Run 12 stress 9.470932e-05
... Procrustes: rmse 0.0001915933 max resid 0.0004653158
... Similar to previous best
Run 13 stress 0.00174054
Run 14 stress 0.002527302
Run 15 stress 0.001775259
Run 16 stress 0.003468988
Run 17 stress 0.003380089
Run 18 stress 0.0009041935
Run 19 stress 0.004412429
Run 20 stress 0.002200873
Run 21 stress 0.002238389
Run 22 stress 9.981772e-05
... Procrustes: rmse 0.0001820388 max resid 0.0003861817
... Similar to previous best
Run 23 stress 0.2809638
Run 24 stress 8.73889e-05
... New best solution
... Procrustes: rmse 0.0001913962 max resid 0.0004004257
... Similar to previous best
*** Solution reachedstress is (nearly) zero: you may have insufficient data
`summarise()` has grouped output by 'seq'. You can override using the `.groups` argument.
some squared distances are negative and changed to zero
Run 0 stress 9.715646e-05
Run 1 stress 0.1650199
Run 2 stress 0.3209238
Run 3 stress 0.001788126
Run 4 stress 9.912951e-05
... Procrustes: rmse 0.06628366 max resid 0.1319275
Run 5 stress 6.098414e-05
... New best solution
... Procrustes: rmse 0.07838964 max resid 0.144714
Run 6 stress 0.0001252884
... Procrustes: rmse 0.0882284 max resid 0.1608503
Run 7 stress 9.793007e-05
... Procrustes: rmse 0.04231372 max resid 0.08277923
Run 8 stress 9.947279e-05
... Procrustes: rmse 0.06478926 max resid 0.09604876
Run 9 stress 9.60471e-05
... Procrustes: rmse 0.1308911 max resid 0.2414282
Run 10 stress 9.691148e-05
... Procrustes: rmse 0.06781632 max resid 0.1158235
Run 11 stress 9.410868e-05
... Procrustes: rmse 0.137796 max resid 0.2929435
Run 12 stress 0.1650199
Run 13 stress 9.998345e-05
... Procrustes: rmse 0.1444623 max resid 0.3319381
Run 14 stress 9.976061e-05
... Procrustes: rmse 0.1624868 max resid 0.3475654
Run 15 stress 9.945545e-05
... Procrustes: rmse 0.0685872 max resid 0.1265135
Run 16 stress 8.382267e-05
... Procrustes: rmse 0.1037207 max resid 0.199643
Run 17 stress 7.592498e-05
... Procrustes: rmse 0.09019872 max resid 0.2078087
Run 18 stress 9.641741e-05
... Procrustes: rmse 0.09552767 max resid 0.2317297
Run 19 stress 0.3209239
Run 20 stress 0.3017346
Run 21 stress 0.0003146586
... Procrustes: rmse 0.05260256 max resid 0.1231596
Run 22 stress 9.958014e-05
... Procrustes: rmse 0.09265857 max resid 0.2163928
Run 23 stress 9.564415e-05
... Procrustes: rmse 0.185973 max resid 0.3985334
Run 24 stress 9.876548e-05
... Procrustes: rmse 0.1348124 max resid 0.2522748
Run 25 stress 0.0002608128
... Procrustes: rmse 0.08627549 max resid 0.1597223
Run 26 stress 9.912719e-05
... Procrustes: rmse 0.08958133 max resid 0.2025491
Run 27 stress 9.959469e-05
... Procrustes: rmse 0.06775683 max resid 0.1344487
Run 28 stress 9.954304e-05
... Procrustes: rmse 0.09906683 max resid 0.2504429
Run 29 stress 9.96858e-05
... Procrustes: rmse 0.1085739 max resid 0.2129246
Run 30 stress 9.328647e-05
... Procrustes: rmse 0.1350035 max resid 0.2374186
Run 31 stress 8.601448e-05
... Procrustes: rmse 0.1225295 max resid 0.2482343
Run 32 stress 0.1650199
Run 33 stress 9.679553e-05
... Procrustes: rmse 0.1079608 max resid 0.2119601
Run 34 stress 9.884363e-05
... Procrustes: rmse 0.07484962 max resid 0.1402191
Run 35 stress 0.1650199
Run 36 stress 0.1650199
Run 37 stress 9.37702e-05
... Procrustes: rmse 0.1283848 max resid 0.1977528
Run 38 stress 9.323913e-05
... Procrustes: rmse 0.08224297 max resid 0.1650759
Run 39 stress 9.61274e-05
... Procrustes: rmse 0.05209736 max resid 0.09928565
Run 40 stress 9.888498e-05
... Procrustes: rmse 0.1288244 max resid 0.2476049
Run 41 stress 9.291898e-05
... Procrustes: rmse 0.1333263 max resid 0.304432
Run 42 stress 0.0004374627
... Procrustes: rmse 0.08095838 max resid 0.1221685
Run 43 stress 9.28992e-05
... Procrustes: rmse 0.06892257 max resid 0.1142821
Run 44 stress 9.780598e-05
... Procrustes: rmse 0.09338538 max resid 0.2228937
Run 45 stress 9.132837e-05
... Procrustes: rmse 0.1787128 max resid 0.3730492
Run 46 stress 9.438506e-05
... Procrustes: rmse 0.1180555 max resid 0.302646
Run 47 stress 8.937148e-05
... Procrustes: rmse 0.1177609 max resid 0.2436194
Run 48 stress 0.0001053967
... Procrustes: rmse 0.107224 max resid 0.2327914
Run 49 stress 9.572375e-05
... Procrustes: rmse 0.08309477 max resid 0.159519
Run 50 stress 9.865761e-05
... Procrustes: rmse 0.1392818 max resid 0.291665
Run 51 stress 9.914654e-05
... Procrustes: rmse 0.05943651 max resid 0.09955262
Run 52 stress 0.0002494261
... Procrustes: rmse 0.1213787 max resid 0.2523826
Run 53 stress 9.388338e-05
... Procrustes: rmse 0.1730467 max resid 0.3814466
Run 54 stress 8.589095e-05
... Procrustes: rmse 0.1472941 max resid 0.3309337
Run 55 stress 0.1650199
Run 56 stress 0.1650199
*** No convergence -- monoMDS stopping criteria:
7: no. of iterations >= maxit
39: stress < smin
7: stress ratio > sratmax
3: scale factor of the gradient < sfgrminstress is (nearly) zero: you may have insufficient data
`summarise()` has grouped output by 'seq'. You can override using the `.groups` argument.
some squared distances are negative and changed to zero
Run 0 stress 0.002262283
Run 1 stress 8.368764e-05
... New best solution
... Procrustes: rmse 0.04257156 max resid 0.08210284
Run 2 stress 0.0002851804
... Procrustes: rmse 0.005632514 max resid 0.01071809
Run 3 stress 0.002943677
Run 4 stress 0.01129741
Run 5 stress 0.3209238
Run 6 stress 0.005096398
Run 7 stress 0.0004746828
... Procrustes: rmse 0.009472061 max resid 0.01814479
Run 8 stress 0.003108479
Run 9 stress 0.0001702689
... Procrustes: rmse 0.003293998 max resid 0.006240563
... Similar to previous best
Run 10 stress 0.0002095137
... Procrustes: rmse 0.004132105 max resid 0.00784589
... Similar to previous best
Run 11 stress 0.002870141
Run 12 stress 0.003772387
Run 13 stress 9.99381e-05
... Procrustes: rmse 0.0009861968 max resid 0.001680875
... Similar to previous best
Run 14 stress 8.706071e-05
... Procrustes: rmse 0.0002403289 max resid 0.0006092737
... Similar to previous best
Run 15 stress 0.0004795237
... Procrustes: rmse 0.009583548 max resid 0.01830686
Run 16 stress 0.003214142
Run 17 stress 0.0003821361
... Procrustes: rmse 0.00763507 max resid 0.01457071
Run 18 stress 0.002209892
Run 19 stress 0.0002180807
... Procrustes: rmse 0.004306652 max resid 0.008182944
... Similar to previous best
Run 20 stress 9.253577e-05
... Procrustes: rmse 0.0002210722 max resid 0.0003899679
... Similar to previous best
Run 21 stress 0.0007126221
Run 22 stress 0.002151844
Run 23 stress 0.003042312
Run 24 stress 0.002676792
*** Solution reachedstress is (nearly) zero: you may have insufficient data
`summarise()` has grouped output by 'seq'. You can override using the `.groups` argument.
Run 0 stress 0.007256534
Run 1 stress 0.0005258356
... New best solution
... Procrustes: rmse 0.08498685 max resid 0.1330745
Run 2 stress 0.0001427363
... New best solution
... Procrustes: rmse 0.004814496 max resid 0.007564717
... Similar to previous best
Run 3 stress 0.003904954
Run 4 stress 0.0003731737
... Procrustes: rmse 0.00308517 max resid 0.004842731
... Similar to previous best
Run 5 stress 0.002248379
Run 6 stress 0.006319521
Run 7 stress 0.002206137
Run 8 stress 0.007584875
Run 9 stress 0.001601641
Run 10 stress 0.004860352
Run 11 stress 0.003032102
Run 12 stress 0.0007629812
Run 13 stress 0.001040513
Run 14 stress 0.003940634
Run 15 stress 0.006256498
Run 16 stress 0.004294185
Run 17 stress 0.00764752
Run 18 stress 0.005972306
Run 19 stress 0.006991458
Run 20 stress 0.001412438
Run 21 stress 0.003185151
Run 22 stress 0.002065363
Run 23 stress 0.3209238
Run 24 stress 0.001362609
*** Solution reachedstress is (nearly) zero: you may have insufficient data
`summarise()` has grouped output by 'seq'. You can override using the `.groups` argument.
Run 0 stress 0.001146298
Run 1 stress 0.0006114779
... New best solution
... Procrustes: rmse 0.07669387 max resid 0.1308177
Run 2 stress 9.840636e-05
... New best solution
... Procrustes: rmse 0.09173068 max resid 0.1748167
Run 3 stress 8.008476e-05
... New best solution
... Procrustes: rmse 0.0115157 max resid 0.02424513
Run 4 stress 9.857295e-05
... Procrustes: rmse 0.102126 max resid 0.2167762
Run 5 stress 0.0005577729
... Procrustes: rmse 0.09007764 max resid 0.2107307
Run 6 stress 0.0006820098
Run 7 stress 0.001055969
Run 8 stress 0.2247492
Run 9 stress 0.0002490661
... Procrustes: rmse 0.0950866 max resid 0.1879391
Run 10 stress 0.0001236696
... Procrustes: rmse 0.07140971 max resid 0.1387873
Run 11 stress 0.000932353
Run 12 stress 9.923291e-05
... Procrustes: rmse 0.1529795 max resid 0.2801125
Run 13 stress 0.001199175
Run 14 stress 5.7183e-05
... New best solution
... Procrustes: rmse 0.1790086 max resid 0.2863295
Run 15 stress 0.0001655761
... Procrustes: rmse 0.1931575 max resid 0.3974455
Run 16 stress 0.0006086659
Run 17 stress 6.240911e-05
... Procrustes: rmse 0.02762979 max resid 0.06639899
Run 18 stress 9.772393e-05
... Procrustes: rmse 0.05410582 max resid 0.1198345
Run 19 stress 0.0008159551
Run 20 stress 9.962655e-05
... Procrustes: rmse 0.1091875 max resid 0.2106903
Run 21 stress 0.001204008
Run 22 stress 9.973635e-05
... Procrustes: rmse 0.1717105 max resid 0.3667307
Run 23 stress 9.579296e-05
... Procrustes: rmse 0.0255935 max resid 0.05148169
Run 24 stress 8.503145e-05
... Procrustes: rmse 0.02377277 max resid 0.05987887
Run 25 stress 9.900052e-05
... Procrustes: rmse 0.02828592 max resid 0.06684035
Run 26 stress 0.0007324996
Run 27 stress 0.0003874549
... Procrustes: rmse 0.1747273 max resid 0.3772563
Run 28 stress 9.232995e-05
... Procrustes: rmse 0.0476767 max resid 0.08280653
Run 29 stress 0.2234307
Run 30 stress 8.940625e-05
... Procrustes: rmse 0.1070105 max resid 0.2460806
Run 31 stress 9.998654e-05
... Procrustes: rmse 0.1598778 max resid 0.3639191
Run 32 stress 9.036142e-05
... Procrustes: rmse 0.2030127 max resid 0.5168994
Run 33 stress 9.99607e-05
... Procrustes: rmse 0.1958642 max resid 0.485691
Run 34 stress 9.56262e-05
... Procrustes: rmse 0.05614366 max resid 0.121565
Run 35 stress 8.504623e-05
... Procrustes: rmse 0.06353254 max resid 0.1538668
Run 36 stress 9.135703e-05
... Procrustes: rmse 0.1452069 max resid 0.2561196
Run 37 stress 0.2892217
Run 38 stress 0.0001173512
... Procrustes: rmse 0.1675575 max resid 0.3527847
Run 39 stress 9.913454e-05
... Procrustes: rmse 0.188475 max resid 0.4655414
Run 40 stress 9.939418e-05
... Procrustes: rmse 0.03167372 max resid 0.0733904
Run 41 stress 0.00128497
Run 42 stress 9.728626e-05
... Procrustes: rmse 0.1867012 max resid 0.3669181
Run 43 stress 9.80644e-05
... Procrustes: rmse 0.08678116 max resid 0.1888868
Run 44 stress 9.709794e-05
... Procrustes: rmse 0.02477011 max resid 0.04748359
Run 45 stress 9.995431e-05
... Procrustes: rmse 0.07585062 max resid 0.1441218
Run 46 stress 0.2233664
Run 47 stress 0.0007823092
Run 48 stress 9.870737e-05
... Procrustes: rmse 0.1908304 max resid 0.4547034
Run 49 stress 0.001056249
Run 50 stress 9.615189e-05
... Procrustes: rmse 0.02080678 max resid 0.05218457
Run 51 stress 9.372442e-05
... Procrustes: rmse 0.1642312 max resid 0.4050803
Run 52 stress 9.741453e-05
... Procrustes: rmse 0.1716041 max resid 0.3460992
Run 53 stress 0.0007855004
Run 54 stress 0.0002756475
... Procrustes: rmse 0.1692496 max resid 0.3648376
Run 55 stress 9.648228e-05
... Procrustes: rmse 0.02304227 max resid 0.05831588
Run 56 stress 9.697157e-05
... Procrustes: rmse 0.02299702 max resid 0.0555012
*** No convergence -- monoMDS stopping criteria:
20: no. of iterations >= maxit
32: stress < smin
1: stress ratio > sratmax
3: scale factor of the gradient < sfgrminstress is (nearly) zero: you may have insufficient data
`summarise()` has grouped output by 'seq'. You can override using the `.groups` argument.
Run 0 stress 0.09468161
Run 1 stress 0.1092528
Run 2 stress 0.09468161
... New best solution
... Procrustes: rmse 4.124787e-06 max resid 8.412769e-06
... Similar to previous best
Run 3 stress 0.09765713
Run 4 stress 0.09468161
... New best solution
... Procrustes: rmse 4.31468e-06 max resid 6.384544e-06
... Similar to previous best
Run 5 stress 0.1041309
Run 6 stress 0.09323891
... New best solution
... Procrustes: rmse 0.1979309 max resid 0.4150472
Run 7 stress 0.09980285
Run 8 stress 0.1092541
Run 9 stress 0.247493
Run 10 stress 0.09468161
Run 11 stress 0.1041307
Run 12 stress 0.1041307
Run 13 stress 0.09765718
Run 14 stress 0.09323787
... New best solution
... Procrustes: rmse 0.0003564355 max resid 0.0007826
... Similar to previous best
Run 15 stress 0.09323929
... Procrustes: rmse 0.0005209651 max resid 0.001123011
... Similar to previous best
Run 16 stress 0.09323846
... Procrustes: rmse 0.0002428062 max resid 0.000483184
... Similar to previous best
Run 17 stress 0.09468168
Run 18 stress 0.09468169
Run 19 stress 0.09323992
... Procrustes: rmse 0.000670326 max resid 0.001453359
... Similar to previous best
Run 20 stress 0.09980303
Run 21 stress 0.09809357
Run 22 stress 0.1092533
Run 23 stress 0.093238
... Procrustes: rmse 3.700314e-05 max resid 7.251982e-05
... Similar to previous best
Run 24 stress 0.09980312
*** Solution reached
`summarise()` has grouped output by 'seq'. You can override using the `.groups` argument.
Run 0 stress 0.02255393
Run 1 stress 0.02255394
... Procrustes: rmse 5.890008e-05 max resid 0.0001230387
... Similar to previous best
Run 2 stress 0.04904836
Run 3 stress 0.02255393
... New best solution
... Procrustes: rmse 1.259295e-05 max resid 1.957772e-05
... Similar to previous best
Run 4 stress 0.02255391
... New best solution
... Procrustes: rmse 1.616999e-05 max resid 3.125392e-05
... Similar to previous best
Run 5 stress 0.02255392
... Procrustes: rmse 3.348823e-05 max resid 7.28367e-05
... Similar to previous best
Run 6 stress 0.02255392
... Procrustes: rmse 3.093981e-05 max resid 6.788791e-05
... Similar to previous best
Run 7 stress 0.0490484
Run 8 stress 0.04904836
Run 9 stress 0.2539725
Run 10 stress 0.04904843
Run 11 stress 0.04904847
Run 12 stress 0.04904836
Run 13 stress 0.02255391
... New best solution
... Procrustes: rmse 1.158434e-05 max resid 2.232686e-05
... Similar to previous best
Run 14 stress 0.02255391
... New best solution
... Procrustes: rmse 1.121923e-05 max resid 2.34532e-05
... Similar to previous best
Run 15 stress 0.04904836
Run 16 stress 0.04904836
Run 17 stress 0.02255392
... Procrustes: rmse 7.145921e-06 max resid 1.240142e-05
... Similar to previous best
Run 18 stress 0.04904836
Run 19 stress 0.02255394
... Procrustes: rmse 4.983671e-05 max resid 0.0001108371
... Similar to previous best
Run 20 stress 0.02255391
... Procrustes: rmse 1.049099e-05 max resid 2.280169e-05
... Similar to previous best
Run 21 stress 0.04904836
Run 22 stress 0.04904852
Run 23 stress 0.02255392
... Procrustes: rmse 2.870542e-05 max resid 6.258551e-05
... Similar to previous best
Run 24 stress 0.04904838
*** Solution reached
`summarise()` has grouped output by 'seq'. You can override using the `.groups` argument.
Run 0 stress 0.0756027
Run 1 stress 0.0756027
... Procrustes: rmse 2.153399e-05 max resid 4.290361e-05
... Similar to previous best
Run 2 stress 0.0756027
... Procrustes: rmse 3.397965e-05 max resid 6.30989e-05
... Similar to previous best
Run 3 stress 0.0756027
... Procrustes: rmse 2.624847e-05 max resid 4.50742e-05
... Similar to previous best
Run 4 stress 0.0756027
... New best solution
... Procrustes: rmse 7.650158e-06 max resid 1.329423e-05
... Similar to previous best
Run 5 stress 0.07560271
... Procrustes: rmse 6.481688e-05 max resid 0.0001202426
... Similar to previous best
Run 6 stress 0.1386357
Run 7 stress 0.07560279
... Procrustes: rmse 0.0001782517 max resid 0.0003279126
... Similar to previous best
Run 8 stress 0.170395
Run 9 stress 0.0756027
... Procrustes: rmse 1.158987e-05 max resid 1.959359e-05
... Similar to previous best
Run 10 stress 0.0756027
... Procrustes: rmse 7.08347e-06 max resid 1.324901e-05
... Similar to previous best
Run 11 stress 0.1386357
Run 12 stress 0.0756027
... Procrustes: rmse 3.313062e-06 max resid 6.033171e-06
... Similar to previous best
Run 13 stress 0.07560276
... Procrustes: rmse 0.0001354551 max resid 0.0002480682
... Similar to previous best
Run 14 stress 0.0756027
... Procrustes: rmse 2.437493e-06 max resid 4.569383e-06
... Similar to previous best
Run 15 stress 0.0756027
... Procrustes: rmse 1.190269e-05 max resid 2.152129e-05
... Similar to previous best
Run 16 stress 0.0756027
... Procrustes: rmse 4.204414e-06 max resid 7.691921e-06
... Similar to previous best
Run 17 stress 0.07560274
... Procrustes: rmse 0.000100163 max resid 0.000181372
... Similar to previous best
Run 18 stress 0.0756027
... Procrustes: rmse 2.642739e-05 max resid 4.885347e-05
... Similar to previous best
Run 19 stress 0.07560271
... Procrustes: rmse 2.941763e-05 max resid 4.499293e-05
... Similar to previous best
Run 20 stress 0.07560271
... Procrustes: rmse 5.29137e-05 max resid 9.774804e-05
... Similar to previous best
Run 21 stress 0.2420206
Run 22 stress 0.0756027
... Procrustes: rmse 1.484627e-05 max resid 2.723898e-05
... Similar to previous best
Run 23 stress 0.0756027
... Procrustes: rmse 4.7863e-05 max resid 8.870301e-05
... Similar to previous best
Run 24 stress 0.0756027
... Procrustes: rmse 2.011567e-05 max resid 3.764673e-05
... Similar to previous best
*** Solution reached
`summarise()` has grouped output by 'seq'. You can override using the `.groups` argument.
Run 0 stress 9.998996e-05
Run 1 stress 8.982222e-05
... New best solution
... Procrustes: rmse 0.04498129 max resid 0.09502346
Run 2 stress 9.458521e-05
... Procrustes: rmse 0.008477069 max resid 0.01793451
Run 3 stress 0.0003032633
... Procrustes: rmse 0.05248099 max resid 0.1090391
Run 4 stress 9.897638e-05
... Procrustes: rmse 0.03773673 max resid 0.07989215
Run 5 stress 0.001029345
Run 6 stress 9.957612e-05
... Procrustes: rmse 0.1431176 max resid 0.2768318
Run 7 stress 0.0003786798
... Procrustes: rmse 0.0107162 max resid 0.01984426
Run 8 stress 9.781639e-05
... Procrustes: rmse 0.03776795 max resid 0.0799525
Run 9 stress 0.000173911
... Procrustes: rmse 0.05407554 max resid 0.1148839
Run 10 stress 0.00102935
Run 11 stress 0.0003584027
... Procrustes: rmse 0.01201585 max resid 0.01989333
Run 12 stress 9.897916e-05
... Procrustes: rmse 0.03773304 max resid 0.07994271
Run 13 stress 0.0001419571
... Procrustes: rmse 0.02190399 max resid 0.04374682
Run 14 stress 0.3043873
Run 15 stress 9.900028e-05
... Procrustes: rmse 0.01481086 max resid 0.02626167
Run 16 stress 0.0006722961
Run 17 stress 9.757683e-05
... Procrustes: rmse 0.0284852 max resid 0.05859385
Run 18 stress 0.001818434
Run 19 stress 9.844341e-05
... Procrustes: rmse 0.03776806 max resid 0.07989408
Run 20 stress 0.0001094373
... Procrustes: rmse 0.08445352 max resid 0.1697529
Run 21 stress 0.0003963194
... Procrustes: rmse 0.0579705 max resid 0.1199147
Run 22 stress 9.534595e-05
... Procrustes: rmse 0.05654676 max resid 0.110169
Run 23 stress 9.597358e-05
... Procrustes: rmse 0.01995265 max resid 0.03912688
Run 24 stress 0.001341222
Run 25 stress 0.000211246
... Procrustes: rmse 0.04055686 max resid 0.08151475
Run 26 stress 9.728825e-05
... Procrustes: rmse 0.008859884 max resid 0.01872675
Run 27 stress 0.0001027518
... Procrustes: rmse 0.1395294 max resid 0.2726191
Run 28 stress 9.312224e-05
... Procrustes: rmse 0.03772878 max resid 0.07987846
Run 29 stress 0.0003249738
... Procrustes: rmse 0.0566021 max resid 0.1186037
Run 30 stress 9.606861e-05
... Procrustes: rmse 0.0377706 max resid 0.07990546
Run 31 stress 8.649417e-05
... New best solution
... Procrustes: rmse 0.03507583 max resid 0.07413743
Run 32 stress 9.596582e-05
... Procrustes: rmse 0.002872238 max resid 0.005883666
... Similar to previous best
*** Solution reachedstress is (nearly) zero: you may have insufficient data
`summarise()` has grouped output by 'seq'. You can override using the `.groups` argument.
Run 0 stress 0.04950647
Run 1 stress 0.0470658
... New best solution
... Procrustes: rmse 0.203486 max resid 0.4359799
Run 2 stress 0.04706575
... New best solution
... Procrustes: rmse 7.451799e-05 max resid 0.0001613821
... Similar to previous best
Run 3 stress 0.04950549
Run 4 stress 0.04950636
Run 5 stress 0.04950542
Run 6 stress 0.04950631
Run 7 stress 0.04950634
Run 8 stress 0.04950591
Run 9 stress 0.0470657
... New best solution
... Procrustes: rmse 0.0008500541 max resid 0.001460748
... Similar to previous best
Run 10 stress 0.04706572
... Procrustes: rmse 3.499225e-05 max resid 7.961403e-05
... Similar to previous best
Run 11 stress 0.04950639
Run 12 stress 0.04950595
Run 13 stress 0.04706519
... New best solution
... Procrustes: rmse 0.0003612806 max resid 0.0006282004
... Similar to previous best
Run 14 stress 0.04950654
Run 15 stress 0.04706524
... Procrustes: rmse 0.0001747118 max resid 0.0002549931
... Similar to previous best
Run 16 stress 0.04950565
Run 17 stress 0.04950565
Run 18 stress 0.04706542
... Procrustes: rmse 0.0002283911 max resid 0.0004007541
... Similar to previous best
Run 19 stress 0.0495053
Run 20 stress 0.04706519
... Procrustes: rmse 1.492559e-05 max resid 3.577865e-05
... Similar to previous best
Run 21 stress 0.04706567
... Procrustes: rmse 0.0004534959 max resid 0.0008160237
... Similar to previous best
Run 22 stress 0.0495053
Run 23 stress 0.04706525
... Procrustes: rmse 0.0002056677 max resid 0.0003663592
... Similar to previous best
Run 24 stress 0.04706544
... Procrustes: rmse 0.0002334217 max resid 0.0004073871
... Similar to previous best
*** Solution reached
`summarise()` has grouped output by 'seq'. You can override using the `.groups` argument.
Run 0 stress 0.05477351
Run 1 stress 0.1007794
Run 2 stress 0.1007795
Run 3 stress 0.05477406
... Procrustes: rmse 0.002319676 max resid 0.005791261
... Similar to previous best
Run 4 stress 0.1226266
Run 5 stress 0.132675
Run 6 stress 0.1007794
Run 7 stress 0.1007794
Run 8 stress 0.2220934
Run 9 stress 0.05477208
... New best solution
... Procrustes: rmse 0.001611524 max resid 0.004029052
... Similar to previous best
Run 10 stress 0.0547719
... New best solution
... Procrustes: rmse 0.000171758 max resid 0.0004222003
... Similar to previous best
Run 11 stress 0.05477239
... Procrustes: rmse 0.001023054 max resid 0.002564228
... Similar to previous best
Run 12 stress 0.2334965
Run 13 stress 0.1007794
Run 14 stress 0.1211966
Run 15 stress 0.1330476
Run 16 stress 0.1882172
Run 17 stress 0.1007794
Run 18 stress 0.1316644
Run 19 stress 0.1007794
Run 20 stress 0.05477446
... Procrustes: rmse 0.001667766 max resid 0.004185369
... Similar to previous best
Run 21 stress 0.05477198
... Procrustes: rmse 0.0007658458 max resid 0.001920631
... Similar to previous best
Run 22 stress 0.05477203
... Procrustes: rmse 0.0007548629 max resid 0.001901816
... Similar to previous best
Run 23 stress 0.1596603
Run 24 stress 0.05477221
... Procrustes: rmse 0.0002422576 max resid 0.0005924589
... Similar to previous best
*** Solution reached
`summarise()` has grouped output by 'seq'. You can override using the `.groups` argument.
Run 0 stress 0.0004631135
Run 1 stress 0.03350798
Run 2 stress 0.0004435229
... New best solution
... Procrustes: rmse 0.1006707 max resid 0.149066
Run 3 stress 0.0003188924
... New best solution
... Procrustes: rmse 0.1122904 max resid 0.1682682
Run 4 stress 0.01733724
Run 5 stress 0.01733875
Run 6 stress 0.030171
Run 7 stress 0.01733898
Run 8 stress 0.0008839934
Run 9 stress 0.01733722
Run 10 stress 0.0002129749
... New best solution
... Procrustes: rmse 0.000685512 max resid 0.001162808
... Similar to previous best
Run 11 stress 0.01733742
Run 12 stress 0.01733715
Run 13 stress 0.03016628
Run 14 stress 0.0002134977
... Procrustes: rmse 0.003469167 max resid 0.006034373
... Similar to previous best
Run 15 stress 0.0004995572
... Procrustes: rmse 0.03805641 max resid 0.06778901
Run 16 stress 0.01733795
Run 17 stress 0.01733842
Run 18 stress 0.000104317
... New best solution
... Procrustes: rmse 0.004241015 max resid 0.007917967
... Similar to previous best
Run 19 stress 0.0002726536
... Procrustes: rmse 0.004443645 max resid 0.008287683
... Similar to previous best
Run 20 stress 0.0007836847
Run 21 stress 0.0006999267
Run 22 stress 0.1820573
Run 23 stress 0.0003537421
... Procrustes: rmse 0.004832757 max resid 0.008882361
... Similar to previous best
Run 24 stress 0.000149104
... Procrustes: rmse 0.003959221 max resid 0.007466465
... Similar to previous best
*** Solution reachedstress is (nearly) zero: you may have insufficient data
[[1]]
[[1]][[1]]
$sites
PCoA1 PCoA2
FRP_3_3 -0.005547325 0.0005045195
FRP_2_3 -0.004488276 0.0065360219
FRP_1_1 0.011477634 -0.0003269092
FRP_1_2 -0.011611522 -0.0066670249
FRP_1_3 -0.000423844 -0.0012355197
FRP_2_1 0.005187778 -0.0014141226
FRP_2_2 0.008881584 -0.0053496524
FRP_3_1 -0.008074636 0.0018758707
FRP_3_2 0.004598607 0.0060768166
$centroids
PCoA1 PCoA2
1 -0.0004238438 -0.001235520
2 0.0051877747 -0.001414123
3 -0.0052787933 0.001403469
attr(,"class")
[1] "ordiplot"
[[1]][[2]]
[[1]][[2]]$x
[1] 0.005396763 0.017771699 0.010382880 0.005816994 0.011580077 0.015433240 0.004486533
[8] 0.013251058 0.017887791 0.015442821 0.008185814 0.013040246 0.018200377 0.007591078
[15] 0.010756449 0.024517523 0.012460151 0.008353108 0.008734111 0.019896871 0.012481253
[22] 0.012562818 0.017985810 0.020873404 0.010194995 0.020826922 0.006533164 0.010448968
[29] 0.009237201 0.010398506 0.006946441 0.014091633 0.009257227 0.019251765 0.012793839
[36] 0.014810298
[[1]][[2]]$y
[1] 0.011275665 0.032428596 0.015822750 0.010198500 0.020162382 0.027125607 0.006426471
[8] 0.023111502 0.033463385 0.025267137 0.013855811 0.023081579 0.032212613 0.010040610
[15] 0.017633458 0.045389253 0.022290796 0.012430187 0.012140820 0.038008904 0.019958792
[22] 0.024728215 0.033060925 0.036986642 0.015438978 0.038901913 0.010254165 0.018553422
[29] 0.015783976 0.015500607 0.009729944 0.026015056 0.015792867 0.033452624 0.024920270
[36] 0.026015080
[[1]][[2]]$yf
[1] 0.010299777 0.032428596 0.015667836 0.010299777 0.020162382 0.026196372 0.006426471
[8] 0.023960392 0.032912308 0.026196372 0.012808940 0.023960392 0.032912308 0.010299777
[15] 0.018093440 0.045389253 0.021124794 0.012808940 0.012808940 0.037965820 0.021124794
[22] 0.023960392 0.032912308 0.037965820 0.015667836 0.037965820 0.010299777 0.018093440
[29] 0.015667836 0.015667836 0.010299777 0.026015056 0.015667836 0.033452624 0.023960392
[36] 0.026015080
[[1]][[3]]
[[2]]
[[2]][[1]]
$sites
PCoA1 PCoA2
FRP_1_1 0.20487958 0.10165109
FRP_1_2 -0.06646233 -0.04206991
FRP_3_3 0.29072760 0.13272390
FRP_1_3 0.16153549 -0.16057535
FRP_2_1 -0.01826230 -0.03584191
FRP_2_2 -0.05973786 -0.19051412
FRP_2_3 -0.06037862 0.03552878
FRP_3_1 -0.31203833 0.13881484
FRP_3_2 -0.14026323 0.02028267
$centroids
PCoA1 PCoA2
1 0.10366541 -0.03119683
2 -0.04701556 -0.07115274
3 -0.10376027 0.07511796
attr(,"class")
[1] "ordiplot"
[[2]][[2]]
[[2]][[2]]$x
[1] 0.3810377 0.2499316 0.3743336 0.3622436 0.4359445 0.3898673 0.5719967 0.4197235 0.4659818
[10] 0.3906026 0.2534814 0.3377097 0.3715432 0.4171329 0.3005332 0.3779329 0.4136108 0.5036109
[19] 0.4488573 0.6220341 0.5149630 0.3549234 0.3675838 0.4202281 0.5742692 0.4649826 0.3320846
[28] 0.3597726 0.4324230 0.3027710 0.3301834 0.4697905 0.3681059 0.4079380 0.3527422 0.3963990
[[2]][[2]]$y
[1] 0.40798746 0.11052997 0.30262601 0.33229061 0.48655804 0.52912930 0.87323323 0.45237770
[9] 0.51612284 0.48180816 0.08347515 0.32803515 0.29834901 0.47113548 0.05031547 0.30844110
[17] 0.43830791 0.55688350 0.60917209 0.97747343 0.56147412 0.40238248 0.33137159 0.40631073
[25] 0.85665036 0.53156880 0.29479708 0.28598772 0.54116654 0.13353068 0.08078603 0.54869919
[33] 0.36102024 0.46803864 0.32205739 0.43569923
[[2]][[2]]$yf
[1] 0.4079875 0.0814402 0.3278086 0.3278086 0.5138623 0.4603509 0.8649418 0.4603509 0.5513907
[10] 0.4603509 0.0814402 0.3250463 0.3278086 0.4603509 0.0814402 0.3278086 0.4603509 0.5568835
[19] 0.5513907 0.9774734 0.5614741 0.3278086 0.3278086 0.4603509 0.8649418 0.5513907 0.2947971
[28] 0.3278086 0.5138623 0.1071584 0.1071584 0.5513907 0.3278086 0.4603509 0.3250463 0.4603509
[[2]][[3]]
[[3]]
[[3]][[1]]
$sites
PCoA1 PCoA2
FRP_3_1 -0.001931237 0.012246537
FRP_1_1 -0.006034021 -0.032972981
FRP_1_3 0.011451056 -0.015143387
FRP_3_2 -0.060762716 0.029059786
FRP_3_3 0.062309100 0.034468914
FRP_1_2 0.013151240 -0.030047175
FRP_2_1 -0.006817757 -0.001986124
FRP_2_2 -0.004472375 0.011522298
FRP_2_3 -0.006893289 -0.007147867
$centroids
PCoA1 PCoA2
1 0.007183662 -0.025043923
2 -0.006380038 -0.001474969
3 -0.001842308 0.015818376
attr(,"class")
[1] "ordiplot"
[[3]][[2]]
[[3]][[2]]$x
[1] 0.06318859 0.04936829 0.07329592 0.07843213 0.06209693 0.05641769 0.05425318 0.04935059
[9] 0.04825470 0.08760758 0.09824942 0.05146963 0.05700800 0.06499605 0.05674382 0.08925677
[17] 0.07714945 0.04326066 0.05134237 0.05455261 0.04908788 0.12390016 0.09773073 0.07868693
[25] 0.07453743 0.08054167 0.08711542 0.08708963 0.08206173 0.08976073 0.06644117 0.06332477
[33] 0.06027328 0.05295257 0.03421111 0.05105556
[[3]][[2]]$y
[1] 1.201968e-05 4.201994e-06 6.665848e-02 6.666525e-02 1.350483e-05 3.674534e-06
[7] 4.357333e-06 6.277624e-06 7.840480e-06 6.665850e-02 6.666835e-02 1.666813e-06
[13] 8.695638e-06 9.660717e-06 6.664432e-06 6.665884e-02 6.666599e-02 9.365316e-06
[19] 1.845371e-06 3.807539e-06 3.189595e-06 1.321780e-01 6.665770e-02 6.665715e-02
[25] 6.665524e-02 6.665609e-02 6.666950e-02 6.666742e-02 6.666913e-02 6.666906e-02
[31] 1.006871e-05 1.084072e-05 7.843553e-06 1.975078e-06 2.605253e-06 3.003779e-06
[[3]][[2]]$yf
[1] 1.121893e-05 4.267121e-06 6.665686e-02 6.666112e-02 1.121893e-05 4.267121e-06
[7] 4.267121e-06 4.267121e-06 4.267121e-06 6.666431e-02 6.666835e-02 4.267121e-06
[13] 8.269595e-06 1.121893e-05 6.664432e-06 6.666431e-02 6.666112e-02 4.267121e-06
[19] 4.267121e-06 4.267121e-06 4.267121e-06 1.321780e-01 6.666431e-02 6.666112e-02
[25] 6.665686e-02 6.666112e-02 6.666431e-02 6.666431e-02 6.666431e-02 6.666431e-02
[31] 1.121893e-05 1.121893e-05 8.269595e-06 4.267121e-06 2.605253e-06 4.267121e-06
[[3]][[3]]
[[4]]
[[4]][[1]]
$sites
PCoA1 PCoA2
FRP_2_2 0.17799255 0.25183997
FRP_3_1 -0.05901937 -0.00314384
FRP_2_3 0.10527545 -0.15435191
FRP_1_2 -0.38534194 0.08012980
FRP_3_2 0.27659212 0.07722254
FRP_1_1 0.10502289 -0.15861495
FRP_1_3 0.05989809 0.02930808
FRP_2_1 0.17327710 -0.09548819
FRP_3_3 -0.45369688 -0.02690150
$centroids
PCoA1 PCoA2
1 0.02194434 -0.01639378
2 0.15360835 -0.06030009
3 -0.05901942 -0.00314379
attr(,"class")
[1] "ordiplot"
[[4]][[2]]
[[4]][[2]]$x
[1] 0.35384841 0.42506718 0.60801190 0.25985575 0.43398207 0.26677392 0.34322605 0.68484990
[9] 0.25619048 0.43431500 0.42063846 0.26697464 0.23898258 0.35873218 0.39788449 0.55392810
[17] 0.28242819 0.07431101 0.20582591 0.17225208 0.56528116 0.66992075 0.55770060 0.48717296
[25] 0.60659752 0.17824954 0.28050839 0.31416901 0.36873608 0.75429305 0.21075361 0.20141782
[33] 0.56696407 0.29137914 0.52747716 0.65931473
[[4]][[2]]$y
[1] 0.64493762 0.62385052 1.20420242 0.27276544 0.62765660 0.42143840 0.73622366 1.32379626
[9] 0.35359124 0.64762321 0.71805405 0.37981572 0.24454404 0.61450308 0.70523771 0.97417919
[17] 0.55027960 0.02634833 0.27780129 0.26102297 0.97830101 1.34425400 0.99992967 0.87725199
[25] 1.22227750 0.24059616 0.54236450 0.47408912 0.56299459 1.42328093 0.29565683 0.23495718
[33] 1.00194072 0.50417357 0.94941327 1.20251638
[[4]][[2]]$yf
[1] 0.63966474 0.66448442 1.20966543 0.31317834 0.66448442 0.40062706 0.63966474 1.33402513
[9] 0.31317834 0.66448442 0.66448442 0.40062706 0.27266739 0.63966474 0.66448442 0.97417919
[17] 0.51772670 0.02634833 0.27266739 0.24552544 0.98911534 1.33402513 0.98911534 0.87725199
[25] 1.20966543 0.24552544 0.51772670 0.51772670 0.63966474 1.42328093 0.27266739 0.24552544
[33] 1.00194072 0.51772670 0.94941327 1.20966543
[[4]][[3]]
[[5]]
[[5]][[1]]
$sites
PCoA1 PCoA2
FRP_1_1 -0.09342640 -0.13909724
FRP_2_1 -0.04911833 -0.05240495
FRP_2_2 -0.10688658 -0.03189644
FRP_2_3 -0.12014178 -0.04200880
FRP_3_1 0.17385655 0.03140832
FRP_3_2 0.27091070 -0.11851037
FRP_1_2 0.08524345 0.17844170
FRP_3_3 0.01561603 0.09419203
FRP_1_3 -0.17605364 0.07987576
$centroids
PCoA1 PCoA2
1 -0.1003262 0.04289651
2 -0.1041637 -0.03904992
3 0.1539449 0.01826174
attr(,"class")
[1] "ordiplot"
[[5]][[2]]
[[5]][[2]]$x
[1] 0.19265351 0.10539408 0.08232738 0.36725262 0.33691756 0.36306676 0.24918064 0.18388904
[9] 0.12686567 0.14959862 0.20880964 0.36200717 0.33287338 0.20899725 0.16125654 0.05258113
[17] 0.32588054 0.38343230 0.28503586 0.17164179 0.10834571 0.31890310 0.40501792 0.30906708
[25] 0.19518097 0.12224410 0.24115522 0.26105703 0.19589059 0.36125654 0.32807336 0.33581472
[33] 0.49118955 0.12687313 0.24675325 0.17444336
[[5]][[2]]$y
[1] 0.19857798 0.15350534 0.12787891 0.62351388 0.73055274 0.68136236 0.44232330 0.28212317
[9] 0.22015337 0.23461059 0.43051379 0.53875978 0.56486107 0.33965397 0.32252581 0.04068146
[17] 0.57556177 0.74404001 0.55251427 0.31723963 0.13050567 0.60965593 0.76742218 0.59284974
[25] 0.35789996 0.15590779 0.30159862 0.40391090 0.36999761 0.59501924 0.70542702 0.63649648
[33] 0.80642784 0.23903972 0.47976165 0.26732717
[[5]][[2]]$yf
[1] 0.27755875 0.14200550 0.12787891 0.65243812 0.62851939 0.65243812 0.44199862 0.27755875
[9] 0.22015337 0.23682516 0.36044100 0.62851939 0.62851939 0.36044100 0.27755875 0.04068146
[17] 0.59268915 0.74404001 0.55251427 0.27755875 0.14200550 0.59268915 0.76742218 0.59268915
[25] 0.35789996 0.15590779 0.36044100 0.44199862 0.36044100 0.62851939 0.62851939 0.62851939
[33] 0.80642784 0.23682516 0.44199862 0.27755875
[[5]][[3]]
[[6]]
[[6]][[1]]
$sites
PCoA1 PCoA2
FRP_3_3 -0.008690301 0.0006200565
FRP_2_2 0.040867132 0.0245199753
FRP_1_1 -0.097763848 -0.0055234469
FRP_2_1 0.023495705 -0.0392895240
FRP_1_3 0.001394753 0.0165320012
FRP_2_3 0.063308584 -0.0106077254
FRP_3_1 0.018009346 0.0400384635
FRP_1_2 -0.006321205 -0.0567512118
FRP_3_2 -0.034300167 0.0304614115
$centroids
PCoA1 PCoA2
1 -0.025141025 -0.014909329
2 0.046035454 -0.007812144
3 -0.009501581 0.023144802
attr(,"class")
[1] "ordiplot"
[[6]][[2]]
[[6]][[2]]$x
[1] 0.08130629 0.10487043 0.06984067 0.08598386 0.09413049 0.08083959 0.10302691 0.07534999
[9] 0.14613330 0.08900176 0.08186569 0.07110255 0.06507338 0.11433579 0.10137255 0.13479296
[17] 0.12121608 0.16455444 0.13419102 0.12655657 0.09723785 0.09691504 0.07625878 0.10397739
[25] 0.09828325 0.10849281 0.09976004 0.06647056 0.09533031 0.08713403 0.08745315 0.10931146
[33] 0.11641142 0.10752060 0.08006624 0.11147665
[[6]][[2]]$y
[1] 0.05524039 0.13509236 0.05907865 0.03298430 0.07572841 0.05081351 0.08125394 0.06862387
[9] 0.17797504 0.07596921 0.05163177 0.04992511 0.03242983 0.13396709 0.08931749 0.18559583
[17] 0.12642262 0.21065367 0.15138150 0.12960012 0.09625803 0.08906042 0.05075685 0.09454591
[25] 0.08654853 0.12757480 0.09117816 0.02814488 0.10755936 0.04136296 0.08075760 0.13336554
[33] 0.13233628 0.13128883 0.05853100 0.12362521
[[6]][[2]]$yf
[1] 0.05189484 0.13018935 0.05189484 0.05189484 0.07748507 0.05189484 0.09159656 0.05189484
[9] 0.18178543 0.07748507 0.05189484 0.05189484 0.03028735 0.13058153 0.09159656 0.18178543
[17] 0.13058153 0.21065367 0.15138150 0.13058153 0.09159656 0.09159656 0.05189484 0.09454591
[25] 0.09159656 0.13018935 0.09159656 0.03028735 0.09159656 0.05189484 0.07748507 0.13018935
[33] 0.13058153 0.13018935 0.05189484 0.13018935
[[6]][[3]]
[[7]]
[[7]][[1]]
$sites
PCoA1 PCoA2
FRP_1_1 0.023601242 0.0515732901
FRP_1_2 -0.114850272 0.0270821925
FRP_1_3 -0.020189353 0.0366346205
FRP_2_1 -0.086838836 -0.0527015210
FRP_2_2 -0.006495302 -0.0154075848
FRP_2_3 0.037056889 -0.0245357974
FRP_3_1 0.033152004 -0.0127861487
FRP_3_2 0.061791096 -0.0006184987
FRP_3_3 0.072772532 -0.0092405525
$centroids
PCoA1 PCoA2
1 -0.025504218 0.03772454
2 -0.007299934 -0.01889747
3 0.059429285 -0.00738656
attr(,"class")
[1] "ordiplot"
[[7]][[2]]
[[7]][[2]]$x
[1] 0.15634170 0.09157809 0.15505331 0.09291704 0.10652995 0.10016082 0.07872819 0.10331525
[9] 0.10857769 0.10505967 0.12734353 0.16579471 0.16242183 0.18530877 0.19368910 0.12515976
[17] 0.08226796 0.10604328 0.09059640 0.10309450 0.11279719 0.10777242 0.14758804 0.14002590
[25] 0.16050291 0.17372128 0.07034092 0.08517276 0.08784587 0.09819521 0.08588402 0.06841429
[33] 0.07430723 0.07033473 0.06263747 0.05360504
[[7]][[2]]$y
[1] 0.23697467 0.09362799 0.23848784 0.09805599 0.11701887 0.08365429 0.05957546 0.10988644
[9] 0.15019554 0.11248847 0.19200864 0.25019816 0.23570915 0.26103648 0.29074122 0.14720848
[17] 0.05656594 0.11515008 0.09123422 0.11103311 0.14550977 0.15434416 0.19659818 0.19769299
[25] 0.23557736 0.24623924 0.06021986 0.04527316 0.08126264 0.09928417 0.03391762 0.06778630
[33] 0.05103516 0.04220719 0.05511256 0.05032557
[[7]][[2]]$yf
[1] 0.23668725 0.09362799 0.23668725 0.09366482 0.11701887 0.09366482 0.05241036 0.11045978
[9] 0.14931449 0.11248847 0.19200864 0.24821870 0.23668725 0.26103648 0.29074122 0.14931449
[17] 0.05241036 0.11515008 0.09123422 0.11045978 0.14931449 0.14931449 0.19714559 0.19714559
[25] 0.23668725 0.24821870 0.05241036 0.05241036 0.08126264 0.09366482 0.05241036 0.05241036
[33] 0.05241036 0.05241036 0.05241036 0.05032557
[[7]][[3]]
[[8]]
[[8]][[1]]
$sites
PCoA1 PCoA2
FRP_1_1 -0.05450767 -0.026592133
FRP_1_2 -0.05358391 -0.023657084
FRP_1_3 -0.02441056 -0.043554241
FRP_2_1 -0.02794855 0.044399105
FRP_2_2 0.05302367 0.006566878
FRP_2_3 -0.05178144 0.038542878
FRP_3_1 0.08979966 0.007271322
FRP_3_2 0.10177646 -0.012233954
FRP_3_3 -0.03236765 0.009257229
$centroids
PCoA1 PCoA2
1 -0.04602763 -3.020217e-02
2 -0.02044166 3.670164e-02
3 0.07545839 -2.907604e-05
attr(,"class")
[1] "ordiplot"
[[8]][[2]]
[[8]][[2]]$x
[1] 0.07515191 0.08546868 0.09879401 0.13508960 0.08987725 0.15978904 0.16653838 0.07942185
[9] 0.09054984 0.09794397 0.13124533 0.09340710 0.16082867 0.16785918 0.08399250 0.10055186
[17] 0.12399559 0.10590683 0.13888111 0.14792764 0.09686048 0.11311481 0.08211352 0.13899424
[25] 0.15580523 0.08689472 0.14121133 0.10060310 0.10003211 0.11898622 0.15580672 0.16809118
[33] 0.08079828 0.07583264 0.14253949 0.14724918
[[8]][[2]]$y
[1] 6.206073e-10 6.228396e-05 4.311712e-05 1.209310e-01 3.909449e-05 1.209715e-01
[7] 1.209909e-01 4.798322e-05 6.228359e-05 4.311654e-05 1.209310e-01 3.909463e-05
[13] 1.209715e-01 1.209909e-01 4.798265e-05 6.384071e-05 1.208899e-01 4.805991e-05
[19] 1.209304e-01 1.209497e-01 3.174363e-05 1.208914e-01 7.209007e-05 1.209319e-01
[25] 1.209513e-01 3.290414e-05 1.209372e-01 5.939098e-05 5.994205e-05 1.208855e-01
[31] 1.209778e-01 1.209971e-01 5.669396e-05 5.045420e-05 1.209261e-01 1.209454e-01
[[8]][[2]]$yf
[1] 6.206073e-10 4.837248e-05 4.837248e-05 1.209308e-01 4.837248e-05 1.209736e-01
[7] 1.209909e-01 4.837248e-05 4.837248e-05 4.837248e-05 1.209308e-01 4.837248e-05
[13] 1.209736e-01 1.209909e-01 4.837248e-05 5.780841e-05 1.208899e-01 5.780841e-05
[19] 1.209308e-01 1.209497e-01 4.837248e-05 1.208885e-01 4.837248e-05 1.209317e-01
[25] 1.209513e-01 4.837248e-05 1.209317e-01 5.780841e-05 5.780841e-05 1.208885e-01
[31] 1.209736e-01 1.209971e-01 4.837248e-05 4.837248e-05 1.209317e-01 1.209454e-01
[[8]][[3]]
[[9]]
[[9]][[1]]
$sites
PCoA1 PCoA2
FRP_2_3 -0.0075069428 -0.0015131609
FRP_3_3 -0.0136314830 -0.0001417953
FRP_2_2 0.0119852752 0.0011382717
FRP_3_1 -0.0149908648 0.0001230846
FRP_3_2 0.0142810121 -0.0061714948
FRP_1_1 0.0268281024 0.0073935921
FRP_2_1 0.0114924888 -0.0037995548
FRP_1_2 -0.0282838824 0.0032418107
FRP_1_3 -0.0001737056 -0.0002707533
$centroids
PCoA1 PCoA2
1 -0.0001737063 -0.0002707522
2 0.0100900982 -0.0013861505
3 -0.0128725480 -0.0003169718
attr(,"class")
[1] "ordiplot"
[[9]][[2]]
[[9]][[2]]$x
[1] 0.009964679 0.020893640 0.011005770 0.024414744 0.036045166 0.021195981 0.024182982
[8] 0.009234508 0.026149051 0.004845268 0.029559353 0.041402984 0.026512302 0.018188682
[15] 0.014446533 0.027029103 0.007041061 0.015520610 0.004760023 0.040311953 0.012498818
[22] 0.030097880 0.042444075 0.027417697 0.015088302 0.015129857 0.018934929 0.007832418
[29] 0.043833057 0.015674168 0.019640032 0.055621287 0.027808151 0.040730605 0.012781774
[36] 0.028652821
[[9]][[2]]$y
[1] 2.033607e-02 3.668644e-02 2.034968e-02 4.537165e-02 7.180295e-02 3.669824e-02
[7] 3.946734e-02 1.956070e-02 4.603613e-02 6.183093e-03 5.962436e-02 7.876267e-02
[13] 4.604305e-02 3.511790e-02 2.533712e-02 5.038645e-02 1.781359e-02 3.513925e-02
[19] 1.468126e-05 7.563339e-02 2.071643e-02 6.289250e-02 8.385799e-02 5.039453e-02
[25] 2.896378e-02 2.973505e-02 3.512724e-02 1.781835e-02 8.460337e-02 3.511599e-02
[31] 3.512703e-02 1.104193e-01 5.446639e-02 7.564405e-02 2.072374e-02 5.610601e-02
[[9]][[2]]$yf
[1] 2.033607e-02 3.668644e-02 2.034968e-02 4.537165e-02 7.180295e-02 3.669824e-02
[7] 3.946734e-02 1.956070e-02 4.603613e-02 6.183093e-03 5.962436e-02 7.876267e-02
[13] 4.604305e-02 3.512438e-02 2.533712e-02 5.038645e-02 1.781359e-02 3.512438e-02
[19] 1.468126e-05 7.563339e-02 2.071643e-02 6.289250e-02 8.385799e-02 5.039453e-02
[25] 2.896378e-02 2.973505e-02 3.512713e-02 1.781835e-02 8.460337e-02 3.512438e-02
[31] 3.512713e-02 1.104193e-01 5.446639e-02 7.564405e-02 2.072374e-02 5.610601e-02
[[9]][[3]]
[[10]]
[[10]][[1]]
$sites
PCoA1 PCoA2
FRP_3_1 -0.1085354 -0.0901112650
FRP_1_1 -0.1098709 0.0682064877
FRP_2_1 -0.1120389 -0.0466669090
FRP_1_2 0.8863304 -0.0004257656
FRP_1_3 -0.1060772 0.1148038400
FRP_2_2 -0.1128938 0.0260613592
FRP_2_3 -0.1128570 -0.0250147296
FRP_3_2 -0.1109676 -0.0654725784
FRP_3_3 -0.1130896 0.0186195607
$centroids
PCoA1 PCoA2
1 -0.08315321 0.08910282
2 -0.11285699 -0.02501473
3 -0.11079026 -0.06328318
attr(,"class")
[1] "ordiplot"
[[10]][[2]]
[[10]][[2]]$x
[1] 0.16549405 0.05199697 0.99909451 0.20778065 0.11850635 0.07043924 0.04520604 0.10783680
[9] 0.12278830 0.99933962 0.08638218 0.05565859 0.10475374 0.14186296 0.05779551 0.99951538
[17] 0.16489914 0.07562484 0.02755773 0.02819011 0.06499279 0.99959390 0.99967155 0.99960898
[25] 0.99959362 0.99947788 0.09769091 0.14266532 0.18389556 0.09994385 0.05749668 0.09462126
[33] 0.01066955 0.04655415 0.04695823 0.08406745
[[10]][[2]]$y
[1] 3.013607e-01 1.284220e-01 5.551949e+02 3.836298e-01 1.945515e-01 1.157876e-01
[7] 1.529731e-01 1.929428e-01 2.183732e-01 5.554368e+02 1.799459e-01 1.255172e-01
[13] 1.961202e-01 2.402494e-01 1.233944e-01 5.553231e+02 3.534321e-01 1.608494e-01
[19] 4.432013e-02 3.933779e-02 1.560427e-01 5.553641e+02 5.553139e+02 5.553056e+02
[25] 5.553422e+02 5.553172e+02 1.969421e-01 3.158175e-01 3.860651e-01 2.006630e-01
[31] 1.201836e-01 1.970179e-01 5.686904e-03 8.355437e-02 1.158210e-01 1.919579e-01
[[10]][[2]]$yf
[1] 3.273964e-01 1.243793e-01 5.551949e+02 3.848474e-01 1.963729e-01 1.359151e-01
[7] 1.174495e-01 1.963729e-01 2.183732e-01 5.553433e+02 1.859519e-01 1.243793e-01
[13] 1.963729e-01 2.402494e-01 1.243793e-01 5.553433e+02 3.273964e-01 1.608494e-01
[19] 4.182896e-02 4.182896e-02 1.359151e-01 5.553433e+02 5.553433e+02 5.553433e+02
[25] 5.553433e+02 5.553433e+02 1.963729e-01 3.158175e-01 3.848474e-01 1.963729e-01
[31] 1.243793e-01 1.963729e-01 5.686904e-03 1.174495e-01 1.174495e-01 1.859519e-01
[[10]][[3]]
[[11]]
[[11]][[1]]
$sites
PCoA1 PCoA2
FRP_1_1 -0.09514701 0.008394403
FRP_1_2 -0.06071760 -0.031882771
FRP_1_3 -0.04370628 0.012410529
FRP_2_1 -0.01669106 0.027673785
FRP_2_2 -0.03552014 -0.037692437
FRP_2_3 0.02258852 0.031983264
FRP_3_1 0.24070467 -0.024534278
FRP_3_2 0.04257802 0.046029929
FRP_3_3 -0.05408912 -0.032382423
$centroids
PCoA1 PCoA2
1 -0.064131389 -0.01106462
2 -0.006827185 0.01209698
3 0.044582793 0.03731557
attr(,"class")
[1] "ordiplot"
[[11]][[2]]
[[11]][[2]]$x
[1] 0.08660329 0.10939189 0.12309256 0.11852043 0.15226257 0.34069878 0.15824574 0.10795226
[9] 0.07844849 0.09340053 0.08260795 0.12660948 0.30333778 0.14198590 0.07380130 0.09256311
[17] 0.11608861 0.11090974 0.29284803 0.13055158 0.09706762 0.10728237 0.08820571 0.26874802
[25] 0.10063465 0.09836585 0.10528789 0.28643948 0.13186197 0.09660365 0.23498865 0.08390076
[33] 0.12702711 0.21693542 0.30100817 0.13891775
[[11]][[2]]$y
[1] 0.0006312815 0.0010005901 0.0012613205 0.0012147068 0.0017887050 0.3856976392
[7] 0.0019976862 0.0006488094 0.0006577296 0.0006525338 0.0006717949 0.0011589744
[13] 0.3850792774 0.0013967194 0.0000175488 0.0006221769 0.0011551701 0.0012627994
[19] 0.3848181357 0.0011729609 0.0006552198 0.0007015049 0.0006443131 0.3844364077
[25] 0.0007441860 0.0006365837 0.0007474602 0.3846856166 0.0012556796 0.0006583741
[31] 0.3839688256 0.0006284788 0.0011414382 0.3837030946 0.3850622844 0.0013807415
[[11]][[2]]$yf
[1] 0.0006426292 0.0010005901 0.0011953386 0.0011953386 0.0017887050 0.3856976392
[7] 0.0019976862 0.0007104901 0.0006426292 0.0006506778 0.0006426292 0.0011953386
[13] 0.3850792774 0.0013967194 0.0000175488 0.0006426292 0.0011953386 0.0011953386
[19] 0.3848181357 0.0011953386 0.0006506778 0.0007104901 0.0006426292 0.3844364077
[25] 0.0007104901 0.0006506778 0.0007104901 0.3846856166 0.0012556796 0.0006506778
[31] 0.3839688256 0.0006426292 0.0011953386 0.3837030946 0.3850622844 0.0013807415
[[11]][[3]]
[[12]]
[[12]][[1]]
$sites
PCoA1 PCoA2
FRP_1_1 0.13421799 -0.02567026
FRP_1_2 -0.07617712 -0.07819529
FRP_1_3 -0.21674975 0.01203261
FRP_2_1 -0.17387372 -0.05905003
FRP_2_2 -0.21539203 0.08890216
FRP_2_3 0.12169325 -0.01426770
FRP_3_1 0.15342130 0.01136414
FRP_3_2 0.13276019 0.01623499
FRP_3_3 0.14009989 0.04864939
$centroids
PCoA1 PCoA2
1 -0.07153641 -0.06026268
2 -0.14846600 -0.01367542
3 0.14185481 0.02265258
attr(,"class")
[1] "ordiplot"
[[12]][[2]]
[[12]][[2]]$x
[1] 0.24588151 0.36140907 0.32526670 0.37797669 0.11084420 0.11211961 0.09870483 0.13075995
[9] 0.20822483 0.15529962 0.22813233 0.23551744 0.27089162 0.24686544 0.25988053 0.15726096
[17] 0.14028697 0.35281988 0.38404300 0.36727377 0.37974387 0.16463695 0.31203666 0.34546287
[25] 0.32766757 0.34706053 0.36144526 0.38551409 0.35939730 0.36720634 0.10868044 0.09415474
[33] 0.12679968 0.10047183 0.12865198 0.12279973
[[12]][[2]]$y
[1] 0.4384089853 0.4791168772 0.4514514986 0.4791395306 0.0001465783 0.0002739245
[7] 0.0001274710 0.0004354685 0.4312987533 0.0683657710 0.4319238224 0.4384787960
[13] 0.4386826268 0.4385092531 0.4387687491 0.3735147496 0.0007288707 0.4790493685
[19] 0.4792807862 0.4791079983 0.4791157429 0.3741713158 0.4515011288 0.4517239003
[25] 0.4515387873 0.4517702762 0.4790718242 0.4793031058 0.4791304584 0.4791377342
[31] 0.0002480599 0.0000587041 0.0003124608 0.0001958896 0.0002718915 0.0003083990
[[12]][[2]]$yf
[1] 0.4384438906 0.4791063866 0.4514763137 0.4791276367 0.0001973191 0.0002739245
[7] 0.0001274710 0.0004354685 0.4312987533 0.0683657710 0.4319238224 0.4384438906
[13] 0.4387256880 0.4385092531 0.4387256880 0.3735147496 0.0007288707 0.4790493685
[19] 0.4792807862 0.4791228662 0.4791276367 0.3741713158 0.4514763137 0.4517239003
[25] 0.4515387873 0.4517702762 0.4791063866 0.4793031058 0.4791063866 0.4791228662
[31] 0.0001973191 0.0000587041 0.0002975838 0.0001958896 0.0002975838 0.0002975838
[[12]][[3]]
[[13]]
[[13]][[1]]
$sites
PCoA1 PCoA2
FRP_1_1 -0.043527957 0.011351539
FRP_1_2 -0.032976299 0.014708119
FRP_1_3 -0.032850891 0.001208606
FRP_2_1 0.009039313 -0.014005872
FRP_2_2 0.013318968 -0.034057330
FRP_2_3 -0.001794822 -0.024393836
FRP_3_1 0.032644030 0.014757002
FRP_3_2 0.020260421 0.001188887
FRP_3_3 0.035887237 0.029242885
$centroids
PCoA1 PCoA2
1 -0.036081254 0.009781335
2 0.007090076 -0.024054386
3 0.030458935 0.015311410
attr(,"class")
[1] "ordiplot"
[[13]][[2]]
[[13]][[2]]$x
[1] 0.06086860 0.07114383 0.08012382 0.08518861 0.07275641 0.08856228 0.08928266 0.09328219
[9] 0.06172993 0.07211751 0.07842937 0.07533160 0.07930017 0.07778332 0.08782613 0.07285193
[17] 0.07791684 0.07531576 0.08535958 0.08282685 0.08922099 0.06112444 0.06361369 0.06988273
[25] 0.06881333 0.06859137 0.06440816 0.06434279 0.07332768 0.07979237 0.07490397 0.07404759
[33] 0.07700380 0.06288039 0.05916057 0.07839304
[[13]][[2]]$y
[1] 0.02155168 0.03313613 0.04622126 0.05364545 0.04305751 0.07181291 0.06838762 0.08689952
[9] 0.01306518 0.03334643 0.04365034 0.03982185 0.05506183 0.04801925 0.07268174 0.03822165
[17] 0.04948345 0.04942874 0.05441125 0.04248422 0.07374824 0.01138114 0.01965011 0.02695631
[25] 0.03347372 0.04071185 0.01652550 0.02688061 0.03996718 0.03474027 0.04308584 0.05306216
[33] 0.05048494 0.02141496 0.02035557 0.04079212
[[13]][[2]]$yf
[1] 0.01658839 0.03352489 0.04587202 0.05364545 0.04041545 0.07165763 0.07165763 0.08689952
[9] 0.01658839 0.03352489 0.04587202 0.04587202 0.04587202 0.04587202 0.07165763 0.04041545
[17] 0.04587202 0.04587202 0.05441125 0.04587202 0.07165763 0.01658839 0.02053253 0.03352489
[25] 0.03352489 0.03352489 0.02170305 0.02170305 0.04041545 0.04587202 0.04587202 0.04587202
[33] 0.04587202 0.02053253 0.01658839 0.04587202
[[13]][[3]]
[[14]]
[[14]][[1]]
$sites
PCoA1 PCoA2
FRP_1_1 0.042477249 0.015020540
FRP_1_3 -0.008705514 -0.011308060
FRP_2_2 -0.039941823 0.016374830
FRP_2_3 -0.011523275 0.017305029
FRP_1_2 -0.026860165 -0.009229914
FRP_2_1 -0.013414343 0.007431860
FRP_3_1 0.036776069 -0.011054285
FRP_3_2 0.030465606 0.003716913
FRP_3_3 -0.009273805 -0.028256913
$centroids
PCoA1 PCoA2
1 -0.007614499 -0.008680049
2 -0.017098149 0.013273441
3 0.025892144 -0.007623800
attr(,"class")
[1] "ordiplot"
[[14]][[2]]
[[14]][[2]]$x
[1] 0.06201681 0.08379503 0.06281956 0.07596679 0.06238529 0.03570633 0.03218882 0.06980164
[9] 0.04985751 0.03545178 0.03152288 0.03054132 0.04921456 0.05136571 0.03170078 0.04338915
[17] 0.03339715 0.04452646 0.08425700 0.07508478 0.05790462 0.04206506 0.02490822 0.06007275
[25] 0.04980744 0.05155351 0.03934190 0.06633336 0.06273824 0.03642909 0.05948173 0.05516107
[33] 0.04257256 0.03229863 0.05593606 0.05422477
[[14]][[2]]$y
[1] 0.08990354 0.14220277 0.08858341 0.11813694 0.08863302 0.04286692 0.02773494 0.09743203
[9] 0.06572478 0.04139409 0.02909842 0.02940850 0.07108072 0.06292971 0.02647966 0.05512236
[17] 0.04404198 0.05363331 0.13458374 0.11943448 0.08519786 0.05021637 0.01214949 0.08977383
[25] 0.06888602 0.06776615 0.03983410 0.09974328 0.09164974 0.04183135 0.08406625 0.06613679
[33] 0.05586437 0.02784326 0.06714696 0.06981000
[[14]][[2]]$yf
[1] 0.08943680 0.13839325 0.09011657 0.11878571 0.08943680 0.04199369 0.02811296 0.09858765
[9] 0.06715531 0.04199369 0.02811296 0.02811296 0.06715531 0.06715531 0.02811296 0.05487335
[17] 0.04199369 0.05487335 0.13839325 0.11878571 0.08463206 0.05021637 0.01214949 0.08943680
[25] 0.06715531 0.06771498 0.04199369 0.09858765 0.09011657 0.04199369 0.08463206 0.06771498
[33] 0.05487335 0.02811296 0.06771498 0.06771498
[[14]][[3]]
[[15]]
[[15]][[1]]
$sites
PCoA1 PCoA2
FRP_1_1 -0.02427211 -0.0123948579
FRP_1_2 -0.05952153 -0.0036819105
FRP_1_3 -0.04205212 -0.0009833522
FRP_2_1 0.04098276 -0.0305990743
FRP_2_2 0.03478885 -0.0025208098
FRP_2_3 0.02023145 -0.0482700382
FRP_3_1 -0.01991263 0.0129203252
FRP_3_2 0.02214500 0.0635419330
FRP_3_3 0.02761034 0.0219877847
$centroids
PCoA1 PCoA2
1 -0.042307299 -0.005225575
2 0.033279852 -0.028778735
3 0.009168741 0.031815020
attr(,"class")
[1] "ordiplot"
[[15]][[2]]
[[15]][[2]]$x
[1] 0.08576216 0.07848963 0.09069074 0.09735769 0.09433685 0.07656739 0.11131934 0.09813733
[9] 0.07561098 0.11808273 0.10886986 0.11029262 0.08818096 0.12274594 0.11237549 0.10493664
[17] 0.10751488 0.09186722 0.07988331 0.10434985 0.10612594 0.07901764 0.07086133 0.09598824
[25] 0.10700354 0.09731846 0.09275430 0.08834169 0.10481250 0.08424695 0.10365324 0.11738720
[33] 0.10538697 0.10073238 0.09728598 0.10315301
[[15]][[2]]$y
[1] 0.04626202 0.02188582 0.05388583 0.06114058 0.04519068 0.02380277 0.08502777 0.06924693
[9] 0.02568341 0.10012724 0.10472714 0.08858142 0.05303957 0.09966930 0.10621787 0.07509350
[17] 0.07907252 0.06662529 0.02884220 0.08385014 0.08186960 0.02452720 0.02786909 0.05693966
[25] 0.09542051 0.04915980 0.05084647 0.05393498 0.07595920 0.02547630 0.05960363 0.11171781
[33] 0.07301046 0.06123406 0.05324179 0.05313534
[[15]][[2]]$yf
[1] 0.04626202 0.02475366 0.05372806 0.06087211 0.05372806 0.02475366 0.09277878 0.06087211
[9] 0.02475366 0.10443306 0.09277878 0.09277878 0.05303957 0.10443306 0.10443306 0.07697833
[17] 0.08724652 0.05372806 0.02715925 0.07697833 0.08186960 0.02475366 0.02475366 0.05372806
[25] 0.08724652 0.05372806 0.05372806 0.05372806 0.07697833 0.02715925 0.06087211 0.10443306
[33] 0.07697833 0.06087211 0.05372806 0.06087211
[[15]][[3]]
[[16]]
[[16]][[1]]
$sites
PCoA1 PCoA2
FRP_1_1 0.3537841 -0.1528578459
FRP_1_2 -0.4443153 0.0038387297
FRP_1_3 -0.4620054 -0.0026233080
FRP_2_1 -0.4328660 -0.0008743293
FRP_2_2 -0.4429111 -0.0059762581
FRP_3_1 0.3366731 0.2130984153
FRP_2_3 0.3603054 0.0351586086
FRP_3_2 0.3653875 0.0295101847
FRP_3_3 0.3659477 -0.1192741969
$centroids
PCoA1 PCoA2
1 -0.4251988 -0.003579352
2 -0.4109515 -0.001837623
3 0.3632207 0.031135354
attr(,"class")
[1] "ordiplot"
[[16]][[2]]
[[16]][[2]]$x
[1] 0.82055054 0.83524384 0.80963083 0.81654136 0.37353272 0.21846695 0.21134329 0.15508352
[9] 0.09821099 0.14626130 0.12856256 0.81383733 0.81211133 0.81642131 0.82697879 0.15288017
[17] 0.13835122 0.83208145 0.83173512 0.83454197 0.84523568 0.09353650 0.80507034 0.80143742
[25] 0.80639881 0.81636467 0.81493099 0.80900714 0.81565615 0.82518040 0.21387904 0.21772577
[33] 0.34441298 0.09457567 0.20328760 0.20024089
[[16]][[2]]$y
[1] 39.2915766 39.3791839 39.2179481 39.2861754 0.6490365 0.3772715 0.2755395 0.1491393
[9] 0.1064509 0.1079765 0.1103326 39.2248480 39.2227116 39.2312678 39.3088011 0.1623112
[17] 0.1055282 39.3114502 39.3097443 39.3184595 39.3961801 0.0749907 39.1499296 39.1483395
[25] 39.1570998 39.2348751 39.2176355 39.2162696 39.2251123 39.3029845 0.2744268 0.3765315
[33] 0.5047577 0.1023846 0.2390443 0.1436170
[[16]][[2]]$yf
[1] 39.2915766 39.3791839 39.2179481 39.2861754 0.6490365 0.3772715 0.2749832 0.1516892
[9] 0.1064509 0.1079765 0.1079304 39.2217317 39.2217317 39.2330715 39.3088011 0.1516892
[17] 0.1079304 39.3114502 39.3097443 39.3184595 39.3961801 0.0749907 39.1499296 39.1483395
[25] 39.1570998 39.2330715 39.2217317 39.2162696 39.2251123 39.3029845 0.2749832 0.3765315
[33] 0.5047577 0.1023846 0.2390443 0.1516892
[[16]][[3]]
[[17]]
[[17]][[1]]
$sites
PCoA1 PCoA2
FRP_1_1 0.01505350 0.059210002
FRP_1_2 -0.14227806 0.034091476
FRP_1_3 -0.03570188 0.031360964
FRP_2_1 -0.10430802 -0.076744177
FRP_2_2 -0.01705433 -0.015335733
FRP_2_3 0.06658511 -0.027376966
FRP_3_1 0.02747575 0.004856602
FRP_3_2 0.07465431 0.008196539
FRP_3_3 0.11557362 -0.018258706
$centroids
PCoA1 PCoA2
1 -0.04252509 0.03644253
2 -0.01705410 -0.01533649
3 0.07468497 0.00368404
attr(,"class")
[1] "ordiplot"
[[17]][[2]]
[[17]][[2]]$x
[1] 0.18814418 0.11438183 0.18502201 0.10144671 0.12301930 0.10484946 0.10878281 0.15711948
[9] 0.13088348 0.13778015 0.14409683 0.22414664 0.18792174 0.22193397 0.26576886 0.14486299
[17] 0.09535830 0.13315769 0.11827795 0.13696799 0.16652484 0.11787937 0.18569309 0.16777719
[25] 0.20374792 0.23559904 0.09527475 0.08841202 0.10733487 0.14268961 0.10219088 0.06237581
[33] 0.07476404 0.08104493 0.11845778 0.07111603
[[17]][[2]]$y
[1] 0.32166028 0.20379367 0.21921166 0.10742709 0.17041519 0.07675010 0.16561887 0.24825111
[9] 0.17453601 0.17644695 0.23717968 0.33935636 0.32163028 0.34845061 0.41919444 0.21353843
[17] 0.09661040 0.16612805 0.17040536 0.17605931 0.24477440 0.19578588 0.32469775 0.26058738
[25] 0.32808305 0.41524470 0.12891696 0.08445182 0.13272349 0.21945912 0.09377060 0.01266765
[33] 0.09055491 0.08893709 0.17341868 0.08844490
[[17]][[2]]$yf
[1] 0.32266277 0.17855435 0.24320614 0.10069503 0.17855435 0.10069503 0.16561887 0.24320614
[9] 0.17855435 0.17855435 0.22535906 0.34390349 0.32266277 0.34390349 0.41919444 0.22535906
[17] 0.10069503 0.17855435 0.17855435 0.17855435 0.24320614 0.17855435 0.32266277 0.24320614
[25] 0.32808305 0.41524470 0.10069503 0.08809718 0.13272349 0.21945912 0.10069503 0.01266765
[33] 0.08809718 0.08809718 0.17855435 0.08809718
[[17]][[3]]
[[18]]
[[18]][[1]]
$sites
PCoA1 PCoA2
FRP_3_1 -0.0490158741 0.069974338
FRP_1_1 0.0521405986 -0.060010873
FRP_1_2 -0.0122007868 -0.035863827
FRP_1_3 -0.0560814854 -0.034592986
FRP_2_1 -0.0568673299 -0.014668197
FRP_2_2 0.0561813829 0.014198967
FRP_2_3 0.0028723044 0.024611023
FRP_3_2 0.0622897858 0.039831333
FRP_3_3 0.0006814045 -0.003479778
$centroids
PCoA1 PCoA2
1 -0.010395929 -0.03975558
2 0.002767008 0.01777500
3 0.003578001 0.03029848
attr(,"class")
[1] "ordiplot"
[[18]][[2]]
[[18]][[2]]$x
[1] 0.16739987 0.11917032 0.11625996 0.11369424 0.12820644 0.09682311 0.13001944 0.10868329
[9] 0.09029646 0.12197112 0.12978463 0.09424100 0.11285375 0.10944510 0.10610557 0.07630210
[17] 0.09860753 0.09716000 0.08778651 0.12829344 0.05962381 0.05959634 0.12454594 0.10430116
[25] 0.14563682 0.09613411 0.13637689 0.09142042 0.13263442 0.10964158 0.08359524 0.06121327
[33] 0.08474156 0.08451367 0.07169515 0.11097404
[[18]][[2]]$y
[1] 0.22259161 0.16465759 0.13529431 0.11563195 0.16716543 0.09726252 0.17282349 0.12610815
[9] 0.08687813 0.16440795 0.18863612 0.09071839 0.12589840 0.12425316 0.09886762 0.07763543
[17] 0.10326807 0.11603056 0.08567575 0.15132178 0.04905376 0.03033327 0.17286705 0.10750747
[25] 0.20228211 0.08970617 0.18491389 0.11168856 0.21072371 0.10363278 0.07920898 0.03681810
[33] 0.08316150 0.09974460 0.03704095 0.11374954
[[18]][[2]]$yf
[1] 0.22259161 0.16408396 0.13529431 0.12076517 0.16408396 0.09734391 0.18072980 0.11693591
[9] 0.08886499 0.16408396 0.18072980 0.09734391 0.12076517 0.11693591 0.10641843 0.07763543
[17] 0.10641843 0.10641843 0.08886499 0.16408396 0.04097094 0.03033327 0.16408396 0.10641843
[25] 0.20228211 0.09734391 0.19781880 0.09734391 0.19781880 0.11693591 0.07920898 0.04097094
[33] 0.08886499 0.08886499 0.04097094 0.11693591
[[18]][[3]]
[[19]]
[[19]][[1]]
$sites
PCoA1 PCoA2
FRP_3_3 0.052988568 -0.0259271679
FRP_3_1 -0.015116037 -0.0022369354
FRP_1_2 -0.036629255 -0.0165120432
FRP_2_1 -0.002453918 0.0166542737
FRP_2_2 0.043587689 0.0138208375
FRP_3_2 -0.047366837 -0.0158009814
FRP_2_3 -0.015422339 0.0149112453
FRP_1_1 0.040909376 -0.0005781309
FRP_1_3 -0.020497246 0.0156689021
$centroids
PCoA1 PCoA2
1 -0.017081124 0.006947953
2 -0.001453417 0.016302932
3 -0.015116035 -0.002236940
attr(,"class")
[1] "ordiplot"
[[19]][[2]]
[[19]][[2]]$x
[1] 0.07375933 0.08979282 0.07134401 0.04395821 0.10307922 0.08082303 0.03377940 0.08549538
[9] 0.03037829 0.02853798 0.06188963 0.03886611 0.02814638 0.05983715 0.02798022 0.05004521
[17] 0.08819445 0.02742754 0.04148364 0.08232858 0.03693615 0.05173173 0.05962204 0.02551725
[25] 0.05070838 0.02962020 0.09689496 0.06533620 0.03006977 0.06563447 0.05142715 0.09097873
[33] 0.04656276 0.06133175 0.02365013 0.06691140
[[19]][[2]]$y
[1] 0.12279749 0.15575899 0.11379533 0.07248771 0.17822877 0.13609601 0.05102552 0.13737189
[9] 0.05102003 0.02407788 0.09751311 0.06625453 0.02408037 0.09160554 0.02408649 0.07504973
[17] 0.14603746 0.02328230 0.06624393 0.13608841 0.06510986 0.07673530 0.08905878 0.02269272
[25] 0.07503954 0.02413835 0.16363236 0.09746251 0.02412996 0.09909466 0.07503812 0.15575915
[33] 0.07359151 0.09751677 0.00165095 0.09903919
[[19]][[2]]$yf
[1] 0.12279749 0.15575899 0.11379533 0.07248771 0.17822877 0.13609221 0.05102552 0.13737189
[9] 0.05102003 0.02408158 0.09749746 0.06624923 0.02408158 0.09160554 0.02408158 0.07504247
[17] 0.14603746 0.02328230 0.06624923 0.13609221 0.06510986 0.07673530 0.08905878 0.02269272
[25] 0.07504247 0.02413416 0.16363236 0.09749746 0.02413416 0.09906693 0.07504247 0.15575915
[33] 0.07359151 0.09749746 0.00165095 0.09906693
[[19]][[3]]
Vouchered Reference dataframe
Here, I’m using a loc list that doesn’t include 16Sfish and crust2 because they have too few samples to include.
apparently, also 18SSSU3
loc_list18
[1] "16SH1" "16Svar" "18Sn4" "L2513H2714" "aquaF2" "aquaF3"
[7] "cep" "ceph16S" "crust16S" "fishcoilbc" "fishminiA" "mifish"
[13] "minibar" "nsCOIFo" "plankCOI" "shark474" "sharkCOImini" "short28S" # cycle over the list of loci for the full reference pool sample replicates
# using the bray-curtis function to test for dissimilarity
# two of the loci were removed because they had too little data remaining after the sodm filter step
lapply(loc_list19, simple.bray, sodm_filtered_df = vrp_sodm_filtered_df, sample = "VRP")`summarise()` has grouped output by 'seq'. You can override using the `.groups` argument.
`summarise()` has grouped output by 'seq'. You can override using the `.groups` argument.
`summarise()` has grouped output by 'seq'. You can override using the `.groups` argument.
`summarise()` has grouped output by 'seq'. You can override using the `.groups` argument.
`summarise()` has grouped output by 'seq'. You can override using the `.groups` argument.
`summarise()` has grouped output by 'seq'. You can override using the `.groups` argument.
`summarise()` has grouped output by 'seq'. You can override using the `.groups` argument.
`summarise()` has grouped output by 'seq'. You can override using the `.groups` argument.
`summarise()` has grouped output by 'seq'. You can override using the `.groups` argument.
`summarise()` has grouped output by 'seq'. You can override using the `.groups` argument.
`summarise()` has grouped output by 'seq'. You can override using the `.groups` argument.
`summarise()` has grouped output by 'seq'. You can override using the `.groups` argument.
`summarise()` has grouped output by 'seq'. You can override using the `.groups` argument.
`summarise()` has grouped output by 'seq'. You can override using the `.groups` argument.
`summarise()` has grouped output by 'seq'. You can override using the `.groups` argument.
`summarise()` has grouped output by 'seq'. You can override using the `.groups` argument.
`summarise()` has grouped output by 'seq'. You can override using the `.groups` argument.
`summarise()` has grouped output by 'seq'. You can override using the `.groups` argument.
`summarise()` has grouped output by 'seq'. You can override using the `.groups` argument.[[1]]
[[2]]
[[3]]
[[4]]
[[5]]
[[6]]
[[7]]
[[8]]
[[9]]
[[10]]
[[11]]
[[12]]
[[13]]
[[14]]
[[15]]
[[16]]
[[17]]
[[18]]
[[19]]NARemove dissimilar replicates
Based on the output from 04-filter-ASV-by-SODM.Rmd, I now want to filter the dataset to remove replicates for loci that have high dissimilarity values, listed here: extdata/downsampled_loci/data/samples_to_remove_downsampled.csv.
# bind together the vouchered and full reference SODM dataframes
reference_df_sodm_filtered <- vrp_sodm_filtered_df %>% bind_rows(frp_sodm_filtered_df)
A clean, non-redundant version of the reference sample dataframe
So that is the dataframe from which we want to remove this particular list of locus-samples
# read in the list of samples to remove
tossers <- read_csv("../extdata/downsampled_loci/data/samples_to_remove_downsampled.csv")Parsed with column specification:
cols(
locus = [31mcol_character()[39m,
sample = [31mcol_character()[39m
)It turns out that an anti-join is all I need for this filtering step.
ref_sodm_bray_filtered_unique <- ref_sodm_filtered_unique %>%
anti_join(., tossers, by = c("locus", "sample"))
Okay, so that is the dataset that I can work through the assessment analyses with, beginning with summary statistics, then adding in the taxonomy and assessing true/false positives in the vouchered samples and breadth of taxonomic coverage in the full reference pool.
Save filtered dataframe
Save the filtered feature table output from occupancy modeling and dissimilarity
# save this version of the feature table to combine with taxonomy for locus-integrated taxonomy
ref_sodm_bray_filtered_unique %>%
saveRDS("../extdata/downsampled_loci/data/feature_table_sodm_bray_filtered.rds", compress = "xz")
---
title: "Filtering ASVs based on dissimilarity across sample replicates"
output: html_notebook
---
20 May 2021

Here we'll use the Bray-Curtis index to identify sample replicates (within each locus) that are more dissimilar than similar to the other replicates. Dissimilarity can be an indication of an issue (contamination, etc.) with a particular replicate.

This uses the output from the species-occupancy detection modeling in `03-species-occupancy-model.Rmd`
The occupancy modeling uses the ASVs (not taxonomy) and I'll use a similar approach here with the Bray-Curtis and NMDS analyses.


## Process

The process is a bit cumbersome because it requires looking at each sample/locus/replicate for the full reference DNA pool and vouchered reference pool.

The process for looking at the dissimilarity among replicates is to:
1. Read in data that has been cleaned up using the occupancy modeling
2. Create a community matrix (per locus)
3. Standardize data across replicates (fct decostand)
4. Generate Bray-Curtis distances (fct vegdist)
   4a. Are any replicates more dissimilar than similar? 
5. Generate NMDS plots from distance matrix (fct metaMDS)

6. Read in list



## Outputs

Based on the NMDS plots and Bray-Curtis dissimilarity index, I generated a list of samples to remove:

`../data/reference_pool_dissimilarity_samples_to_remove.csv`

The data that were used to generate that list are output .csv files from the Bray-Curtis function, implemented below.

In addition, three loci had insufficient data across all 18 samples in both the vouchered and full reference pools to be included in these analyses and will be dropped from further analyses:

16Sfish
teleo
crust2


```{r load-functions}
source("../R/metabarcoding-funcs.R")
```



```{r load-libs-and-data}
library(tidyverse)
library(stringi)
library(vegan)
library(reshape2)
library(textshape)
library(rlist)


# output from the ASV filtering based on the SODM for 
# vouchered ref
vrp_sodm_filtered_df <- readRDS("../extdata/downsampled_loci/data/voucher_features_sodm_filtered_taxonomy_df.rds")

# full reference
frp_sodm_filtered_df <- readRDS("../extdata/downsampled_loci/data/full_reference_sodm_filtered_taxonomy_df.rds")

```


I have wrapped the Bray-Curis and NMDS up into a function called `bray_nmds_complete` which outputs a .csv file with the replicates that are > 0.49 dissimilar and generates an NMDS plot.


I'll use that function with an lappy and the list of loci, since each locus will be analyzed separately. 

To cycle over a list of the loci...
```{r}
# grab the names of the loci from the full dataframe
locs <- frp_sodm_filtered_df %>%
  select(locus) %>%
  unique() %>%
  as.list()

# turn that into a list that could be cycled over
loc_list <- locs$locus

# make a separate list for the VRP samples
locs19 <- frp_sodm_filtered_df %>%
  select(locus) %>%
  unique() %>%
  filter(!locus %in% c("crust2", "16Sfish", "teleo")) %>%
  as.list()

# turn that into a list that could be cycled over
loc_list19 <- locs19$locus

```



## Full Reference dataframe

```{r apply-bray-fct-over-full-reference-pool}
# cycle over the list of loci for the full reference pool sample replicates
# using the bray-curtis function to test for dissimilarity
lapply(loc_list19, bray_nmds_complete, sodm_filtered_df = frp_sodm_filtered_df, sample = "FRP")
```

## Vouchered Reference dataframe

Here, I'm using a loc list that doesn't include 16Sfish and crust2 because they have too few samples to include. 

apparently, also 18SSSU3

```{r}
# make a separate list for the VRP samples
locs18 <- frp_sodm_filtered_df %>%
  select(locus) %>%
  unique() %>%
  filter(!locus %in% c("crust2", "16Sfish", "teleo", "18SSSU3")) %>%
  as.list()

# turn that into a list that could be cycled over
loc_list18 <- locs18$locus

```


```{r apply-bray-filter-vouchered-samples}
# cycle over the list of loci for the full reference pool sample replicates
# using the bray-curtis function to test for dissimilarity
# two of the loci were removed because they had too little data remaining after the sodm filter step
lapply(loc_list19, simple.bray, sodm_filtered_df = vrp_sodm_filtered_df, sample = "VRP")

```



## Remove dissimilar replicates

Based on the output from `04-filter-ASV-by-SODM.Rmd`, I now want to filter the dataset to remove replicates for loci that have high dissimilarity values, listed here: `extdata/downsampled_loci/data/samples_to_remove_downsampled.csv`.


```{r bind-reference-dataframes}
# bind together the vouchered and full reference SODM dataframes
reference_df_sodm_filtered <- vrp_sodm_filtered_df %>% bind_rows(frp_sodm_filtered_df)

```


A clean, non-redundant version of the reference sample dataframe
```{r}
ref_sodm_filtered_unique <- reference_df_sodm_filtered %>% 
  select(locus, seq, sample, count) %>%
  unique() %>% # if there are multiple entries with different counts, we want to collapse those reads
  group_by(locus, seq, sample) %>%
  mutate(total_reads = sum(count)) %>%
  select(-count) %>%
  rename(count = total_reads)

```

So that is the dataframe from which we want to remove this particular list of locus-samples
```{r dissimilar-samples-to-remove}
# read in the list of samples to remove
tossers <- read_csv("../extdata/downsampled_loci/data/samples_to_remove_downsampled.csv")

```

It turns out that an anti-join is all I need for this filtering step.
```{r}
ref_sodm_bray_filtered_unique <- ref_sodm_filtered_unique %>%
  anti_join(., tossers, by = c("locus", "sample")) 

```

Okay, so that is the dataset that I can work through the assessment analyses with, beginning with summary statistics, then adding in the taxonomy and assessing true/false positives in the vouchered samples and breadth of taxonomic coverage in the full reference pool.


## Save filtered dataframe

Save the filtered feature table output from occupancy modeling and dissimilarity
```{r}
# save this version of the feature table to combine with taxonomy for locus-integrated taxonomy
ref_sodm_bray_filtered_unique %>%
  saveRDS("../extdata/downsampled_loci/data/feature_table_sodm_bray_filtered.rds", compress = "xz")

```
